id string | sources list | title string | abstract string | authors list | categories list | fields_of_study list | published_date timestamp[s] | url string | pdf_url string | arxiv_id string | doi string | citation_count int64 | influential_citation_count int64 | has_code bool | code_url string | venue string | quality_score float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
778030e3203f938a87b9c05a13afd15aa5c9078c49693d86abac7e19ed422dd5 | [
"arxiv",
"semantic_scholar"
] | CoheMark: A Novel Sentence-Level Watermark for Enhanced Text Quality | Watermarking technology is a method used to trace the usage of content generated by large language models. Sentence-level watermarking aids in preserving the semantic integrity within individual sentences while maintaining greater robustness. However, many existing sentence-level watermarking techniques depend on arbit... | [
"Junyan Zhang",
"Shuliang Liu",
"Aiwei Liu",
"Yubo Gao",
"Jungang Li",
"Xiaojie Gu",
"Xuming Hu"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-04-24T00:00:00 | https://arxiv.org/abs/2504.17309 | https://arxiv.org/pdf/2504.17309v1 | 2504.17309 | 10.48550/arXiv.2504.17309 | 17 | 0 | false | null | arXiv.org | 0.3138 |
7b15a966dc1b665f48b2edebb8b0b050830e6a73e22ca407e4b5f9791f179e20 | [
"arxiv",
"semantic_scholar"
] | Tracing Thought: Using Chain-of-Thought Reasoning to Identify the LLM Behind AI-Generated Text | In recent years, the detection of AI-generated text has become a critical area of research due to concerns about academic integrity, misinformation, and ethical AI deployment. This paper presents COT Fine-tuned, a novel framework for detecting AI-generated text and identifying the specific language model. responsible f... | [
"Shifali Agrahari",
"Sanasam Ranbir Singh"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-04-23T00:00:00 | https://arxiv.org/abs/2504.16913 | https://arxiv.org/pdf/2504.16913v1 | 2504.16913 | 10.48550/arXiv.2504.16913 | 4 | 0 | false | null | arXiv.org | 0.1747 |
2f7a97ca606d6c2d46039a513ef7be2c03f3294135933623f50f6a460ad90ef9 | [
"arxiv",
"semantic_scholar"
] | Certified Mitigation of Worst-Case LLM Copyright Infringement | The exposure of large language models (LLMs) to copyrighted material during pre-training raises concerns about unintentional copyright infringement post deployment. This has driven the development of "copyright takedown" methods, post-training approaches aimed at preventing models from generating content substantially ... | [
"Jingyu Zhang",
"Jiacan Yu",
"Marc Marone",
"Benjamin Van Durme",
"Daniel Khashabi"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-04-22T00:00:00 | https://arxiv.org/abs/2504.16046 | https://arxiv.org/pdf/2504.16046v2 | 2504.16046 | 10.48550/arXiv.2504.16046 | 2 | 0 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.1341 |
179efa13e92998216b96e3eeed219b76d226bfe16d6c5a8404c762a8cdb5c0f2 | [
"arxiv",
"semantic_scholar"
] | Entropy-Guided Watermarking for LLMs: A Test-Time Framework for Robust and Traceable Text Generation | The rapid development of Large Language Models (LLMs) has intensified concerns about content traceability and potential misuse. Existing watermarking schemes for sampled text often face trade-offs between maintaining text quality and ensuring robust detection against various attacks. To address these issues, we propose... | [
"Shizhan Cai",
"Liang Ding",
"Dacheng Tao"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-04-16T00:00:00 | https://arxiv.org/abs/2504.12108 | https://arxiv.org/pdf/2504.12108v1 | 2504.12108 | 10.48550/arXiv.2504.12108 | 1 | 0 | false | null | arXiv.org | 0.1272 |
c97ed61161f2ad2c3f0c4982cefaa8358ada8a37f604d91c485edb2694226f65 | [
"arxiv",
"semantic_scholar"
] | Watermarking Needs Input Repetition Masking | Recent advancements in Large Language Models (LLMs) raised concerns over potential misuse, such as for spreading misinformation. In response two counter measures emerged: machine learning-based detectors that predict if text is synthetic, and LLM watermarking, which subtly marks generated text for identification and at... | [
"David Khachaturov",
"Robert Mullins",
"Ilia Shumailov",
"Sumanth Dathathri"
] | [
"cs.LG",
"cs.CL",
"cs.CR"
] | [
"Computer Science"
] | 2025-04-16T00:00:00 | https://arxiv.org/abs/2504.12229 | https://arxiv.org/pdf/2504.12229v1 | 2504.12229 | 10.48550/arXiv.2504.12229 | 0 | 0 | false | null | arXiv.org | 0.1272 |
5042af3ac013f1bb90a67f19461300048d8ac92112895e23465847a0ca4bcc76 | [
"arxiv",
"semantic_scholar"
] | Deep Audio Watermarks are Shallow: Limitations of Post-Hoc Watermarking Techniques for Speech | In the audio modality, state-of-the-art watermarking methods leverage deep neural networks to allow the embedding of human-imperceptible signatures in generated audio. The ideal is to embed signatures that can be detected with high accuracy when the watermarked audio is altered via compression, filtering, or other tran... | [
"Patrick O'Reilly",
"Zeyu Jin",
"Jiaqi Su",
"Bryan Pardo"
] | [
"cs.SD",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2025-04-15T00:00:00 | https://arxiv.org/abs/2504.10782 | https://arxiv.org/pdf/2504.10782v1 | 2504.10782 | 10.48550/arXiv.2504.10782 | 13 | 3 | false | null | arXiv.org | 0.301 |
3585b2e0595849b83dec182122f4e903ab4e407291fa63d6a1fe1ece7f7402b4 | [
"arxiv",
"semantic_scholar"
] | PT-Mark: Invisible Watermarking for Text-to-image Diffusion Models via Semantic-aware Pivotal Tuning | Watermarking for diffusion images has drawn considerable attention due to the widespread use of text-to-image diffusion models and the increasing need for their copyright protection. Recently, advanced watermarking techniques, such as Tree Ring, integrate watermarks by embedding traceable patterns (e.g., Rings) into th... | [
"Yaopeng Wang",
"Huiyu Xu",
"Zhibo Wang",
"Jiacheng Du",
"Zhichao Li",
"Yiming Li",
"Qiu Wang",
"Kui Ren"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2025-04-15T00:00:00 | https://arxiv.org/abs/2504.10853 | https://arxiv.org/pdf/2504.10853v2 | 2504.10853 | 10.48550/arXiv.2504.10853 | 2 | 0 | false | null | IEEE Transactions on Dependable and Secure Computing | 0.126 |
e8d204959e84a9c1683c0c7cde69969d9ea84e7e932db487c92385f7b95f770c | [
"arxiv",
"semantic_scholar"
] | OpenTuringBench: An Open-Model-based Benchmark and Framework for Machine-Generated Text Detection and Attribution | Open Large Language Models (OLLMs) are increasingly leveraged in generative AI applications, posing new challenges for detecting their outputs. We propose OpenTuringBench, a new benchmark based on OLLMs, designed to train and evaluate machine-generated text detectors on the Turing Test and Authorship Attribution proble... | [
"Lucio La Cava",
"Andrea Tagarelli"
] | [
"cs.CL",
"cs.AI",
"cs.CY",
"cs.HC",
"physics.soc-ph"
] | [
"Computer Science",
"Physics"
] | 2025-04-15T00:00:00 | https://arxiv.org/abs/2504.11369 | https://arxiv.org/pdf/2504.11369v1 | 2504.11369 | 10.48550/arXiv.2504.11369 | 6 | 0 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.2113 |
8309d676b7fc480394f73eb321ac9928918bd06af18dda08147d1034ec9a84e1 | [
"arxiv",
"semantic_scholar"
] | FractalForensics: Proactive Deepfake Detection and Localization via Fractal Watermarks | Proactive Deepfake detection via robust watermarks has seen interest ever since passive Deepfake detectors encountered challenges in identifying high-quality synthetic images. However, while demonstrating reasonable detection performance, they lack localization functionality and explainability in detection results. Add... | [
"Tianyi Wang",
"Harry Cheng",
"Ming-Hui Liu",
"Mohan Kankanhalli"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-04-13T00:00:00 | https://arxiv.org/abs/2504.09451 | https://arxiv.org/pdf/2504.09451v2 | 2504.09451 | 10.1145/3746027.3754544 | 11 | 0 | false | null | ACM Multimedia | 0.2698 |
7b1c098849718a962e5d228a4f7d38dfa50e8273c0f35fe93b64a0111a157ab1 | [
"arxiv",
"semantic_scholar"
] | Defending LLM Watermarking Against Spoofing Attacks with Contrastive Representation Learning | Watermarking has emerged as a promising technique for detecting texts generated by LLMs. Current research has primarily focused on three design criteria: high quality of the watermarked text, high detectability, and robustness against removal attack. However, the security against spoofing attacks remains relatively und... | [
"Li An",
"Yujian Liu",
"Yepeng Liu",
"Yang Zhang",
"Yuheng Bu",
"Shiyu Chang"
] | [
"cs.CR",
"cs.CL"
] | [
"Computer Science"
] | 2025-04-09T00:00:00 | https://arxiv.org/abs/2504.06575 | https://arxiv.org/pdf/2504.06575v2 | 2504.06575 | 10.48550/arXiv.2504.06575 | 10 | 0 | true | https://github.com/UCSB-NLP-Chang/contrastive-watermark | arXiv.org | 0.2603 |
88f04e47077a1a5556e8709b89485451a679021b0cea91ce916f027fafa2d527 | [
"arxiv",
"semantic_scholar"
] | DeCoMa: Detecting and Purifying Code Dataset Watermarks through Dual Channel Code Abstraction | Watermarking is a technique to help identify the source of data points, which can be used to help prevent the misuse of protected datasets. Existing methods on code watermarking, leveraging the idea from the backdoor research, embed stealthy triggers as watermarks. Despite their high resilience against dilution attacks... | [
"Yuan Xiao",
"Yuchen Chen",
"Shiqing Ma",
"Haocheng Huang",
"Chunrong Fang",
"Yanwei Chen",
"Weisong Sun",
"Yunfeng Zhu",
"Xiaofang Zhang",
"Zhenyu Chen"
] | [
"cs.CR",
"cs.SE"
] | [
"Computer Science"
] | 2025-04-09T00:00:00 | https://arxiv.org/abs/2504.07002 | https://arxiv.org/pdf/2504.07002v2 | 2504.07002 | 10.1145/3728952 | 6 | 0 | true | https://github.com/xiaoyuanpigo/DeCoMa | null | 0.2113 |
acdea3d4c6d07bfaebc5ab2f43be81257a325da4c01b8f9cfaa8a8ef1413dbc5 | [
"arxiv",
"semantic_scholar"
] | Can you Finetune your Binoculars? Embedding Text Watermarks into the Weights of Large Language Models | The indistinguishability of AI-generated content from human text raises challenges in transparency and accountability. While several methods exist to watermark models behind APIs, embedding watermark strategies directly into model weights that are later reflected in the outputs of the model is challenging. In this stud... | [
"Fay Elhassan",
"Niccolò Ajroldi",
"Antonio Orvieto",
"Jonas Geiping"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-04-08T00:00:00 | https://arxiv.org/abs/2504.06446 | https://arxiv.org/pdf/2504.06446v1 | 2504.06446 | 10.48550/arXiv.2504.06446 | 3 | 0 | false | null | arXiv.org | 0.1505 |
e3c9e42c9bec62cfb22e4ae95ceb67f07e291805fd0634a96b49a7ab1413e287 | [
"arxiv",
"semantic_scholar"
] | Detection Limits and Statistical Separability of Tree Ring Watermarks in Rectified Flow-based Text-to-Image Generation Models | Tree-Ring Watermarking is a significant technique for authenticating AI-generated images. However, its effectiveness in rectified flow-based models remains unexplored, particularly given the inherent challenges of these models with noise latent inversion. Through extensive experimentation, we evaluated and compared the... | [
"Ved Umrajkar",
"Aakash Kumar Singh"
] | [
"cs.CV",
"cs.AI",
"cs.CR",
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2025-04-04T00:00:00 | https://arxiv.org/abs/2504.03850 | https://arxiv.org/pdf/2504.03850v1 | 2504.03850 | 10.48550/arXiv.2504.03850 | 0 | 0 | true | https://github.com/dsgiitr/flux-watermarking}{\textbf{link}} | arXiv.org | 0.1753 |
12411a5f5d1cfce249e6a33cf00bb1583cda3f4cca00bfef47ffbf31773a7981 | [
"arxiv",
"semantic_scholar"
] | FontGuard: A Robust Font Watermarking Approach Leveraging Deep Font Knowledge | The proliferation of AI-generated content brings significant concerns on the forensic and security issues such as source tracing, copyright protection, etc, highlighting the need for effective watermarking technologies. Font-based text watermarking has emerged as an effective solution to embed information, which could ... | [
"Kahim Wong",
"Jicheng Zhou",
"Kemou Li",
"Yain-Whar Si",
"Xiaowei Wu",
"Jiantao Zhou"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-04-04T00:00:00 | https://arxiv.org/abs/2504.03128 | https://arxiv.org/pdf/2504.03128v1 | 2504.03128 | 10.1109/TMM.2025.3604908 | 3 | 0 | true | https://github.com/KAHIMWONG/FontGuard | IEEE transactions on multimedia | 0.1753 |
82edbec16fa4114262b1378f062aa75da0b6560b22f3a9b8ab1b03c547166ebb | [
"arxiv",
"semantic_scholar"
] | RoSMM: A Robust and Secure Multi-Modal Watermarking Framework for Diffusion Models | Current image watermarking technologies are predominantly categorized into text watermarking techniques and image steganography; however, few methods can simultaneously handle text and image-based watermark data, which limits their applicability in complex digital environments. This paper introduces an innovative multi... | [
"ZhongLi Fang",
"Yu Xie",
"Ping Chen"
] | [
"cs.MM"
] | [
"Computer Science"
] | 2025-04-03T00:00:00 | https://arxiv.org/abs/2504.02640 | https://arxiv.org/pdf/2504.02640v2 | 2504.02640 | 10.48550/arXiv.2504.02640 | 1 | 0 | false | null | arXiv.org | 0.1123 |
a588235413e0feab72976d9c29357112fe3419d3e3417cb50496e2dbc73e2f7f | [
"arxiv",
"semantic_scholar"
] | Watermarking for AI Content Detection: A Review on Text, Visual, and Audio Modalities | The rapid advancement of generative artificial intelligence (GenAI) has revolutionized content creation across text, visual, and audio domains, simultaneously introducing significant risks such as misinformation, identity fraud, and content manipulation. This paper presents a practical survey of watermarking techniques... | [
"Lele Cao"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2025-04-02T00:00:00 | https://arxiv.org/abs/2504.03765 | https://arxiv.org/pdf/2504.03765v1 | 2504.03765 | 10.48550/arXiv.2504.03765 | 7 | 0 | false | null | arXiv.org | 0.2258 |
13baafc28ce03dcb5a3003fcdba71a792a1eaf15a3504924cb2c25efa63755ca | [
"arxiv",
"semantic_scholar"
] | Understanding the Effects of RLHF on the Quality and Detectability of LLM-Generated Texts | Large Language Models (LLMs) have demonstrated exceptional performance on a range of downstream NLP tasks by generating text that closely resembles human writing. However, the ease of achieving this similarity raises concerns from potential malicious uses at scale by bad actors, as LLM-generated text becomes increasing... | [
"Beining Xu",
"Arkaitz Zubiaga"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-03-23T00:00:00 | https://arxiv.org/abs/2503.17965 | https://arxiv.org/pdf/2503.17965v1 | 2503.17965 | 10.48550/arXiv.2503.17965 | 3 | 2 | false | null | arXiv.org | 0.2386 |
ba550fad2988da3213f757a516da11a5b591a39177cfdf6f31aeb2cbb6ddcefa | [
"arxiv",
"semantic_scholar"
] | Text-Guided Image Invariant Feature Learning for Robust Image Watermarking | Ensuring robustness in image watermarking is crucial for and maintaining content integrity under diverse transformations. Recent self-supervised learning (SSL) approaches, such as DINO, have been leveraged for watermarking but primarily focus on general feature representation rather than explicitly learning invariant f... | [
"Muhammad Ahtesham",
"Xin Zhong"
] | [
"cs.CV",
"cs.LG",
"cs.MM"
] | [
"Computer Science"
] | 2025-03-18T00:00:00 | https://arxiv.org/abs/2503.13805 | https://arxiv.org/pdf/2503.13805v1 | 2503.13805 | 10.1109/MIPR67560.2025.00033 | 2 | 0 | false | null | Conference on Multimedia Information Processing and Retrieval | 0.1193 |
e0a46955eff0a40603425abdc87ca5dca4a5bb8f35050b8c0b05d27f2afdcd64 | [
"arxiv",
"semantic_scholar"
] | Your Text Encoder Can Be An Object-Level Watermarking Controller | Invisible watermarking of AI-generated images can help with copyright protection, enabling detection and identification of AI-generated media. In this work, we present a novel approach to watermark images of T2I Latent Diffusion Models (LDMs). By only fine-tuning text token embeddings $W_*$, we enable watermarking in s... | [
"Naresh Kumar Devulapally",
"Mingzhen Huang",
"Vishal Asnani",
"Shruti Agarwal",
"Siwei Lyu",
"Vishnu Suresh Lokhande"
] | [
"cs.CV",
"cs.CR",
"cs.LG"
] | [
"Computer Science"
] | 2025-03-15T00:00:00 | https://arxiv.org/abs/2503.11945 | https://arxiv.org/pdf/2503.11945v1 | 2503.11945 | 10.1109/ICCV51701.2025.01539 | 1 | 0 | false | null | IEEE International Conference on Computer Vision | 0.0905 |
81cd6cb49bede85aa59c39128cf773d92a87d0e38cdac74fc899a25d6d075b33 | [
"arxiv",
"semantic_scholar"
] | Safe-VAR: Safe Visual Autoregressive Model for Text-to-Image Generative Watermarking | With the success of autoregressive learning in large language models, it has become a dominant approach for text-to-image generation, offering high efficiency and visual quality. However, invisible watermarking for visual autoregressive (VAR) models remains underexplored, despite its importance in misuse prevention. Ex... | [
"Ziyi Wang",
"Songbai Tan",
"Gang Xu",
"Xuerui Qiu",
"Hongbin Xu",
"Xin Meng",
"Ming Li",
"Fei Richard Yu"
] | [
"cs.MM",
"cs.CV",
"eess.IV"
] | [
"Computer Science",
"Engineering"
] | 2025-03-14T00:00:00 | https://arxiv.org/abs/2503.11324 | https://arxiv.org/pdf/2503.11324v1 | 2503.11324 | 10.48550/arXiv.2503.11324 | 3 | 0 | false | null | arXiv.org | 0.1505 |
1725e205d4ae5488bac5927e2231d9fe9eb29a0acd3819a81a862325281ad22b | [
"arxiv",
"semantic_scholar"
] | Pathology-Aware Adaptive Watermarking for Text-Driven Medical Image Synthesis | As recent text-conditioned diffusion models have enabled the generation of high-quality images, concerns over their potential misuse have also grown. This issue is critical in the medical domain, where text-conditioned generated medical images could enable insurance fraud or falsified records, highlighting the urgent n... | [
"Chanyoung Kim",
"Dayun Ju",
"Jinyeong Kim",
"Woojung Han",
"Roberto Alcover-Couso",
"Seong Jae Hwang"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-03-11T00:00:00 | https://arxiv.org/abs/2503.08346 | https://arxiv.org/pdf/2503.08346v2 | 2503.08346 | 10.48550/arXiv.2503.08346 | 1 | 0 | false | null | International Conference on Medical Image Computing and Computer-Assisted Intervention | 0.0859 |
c7654a0ee51f054f15dd168a4642194c9293349ebe102b36b252eb30a89be525 | [
"arxiv",
"semantic_scholar"
] | Fine-Grained Bias Detection in LLM: Enhancing detection mechanisms for nuanced biases | Recent advancements in Artificial Intelligence, particularly in Large Language Models (LLMs), have transformed natural language processing by improving generative capabilities. However, detecting biases embedded within these models remains a challenge. Subtle biases can propagate misinformation, influence decision-maki... | [
"Suvendu Mohanty"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-03-08T00:00:00 | https://arxiv.org/abs/2503.06054 | https://arxiv.org/pdf/2503.06054v1 | 2503.06054 | 10.48550/arXiv.2503.06054 | 3 | 1 | false | null | arXiv.org | 0.1505 |
3152585a56521600498c3c475abd496ab75a2ffd8619a479c4685fe673ceb2f2 | [
"arxiv",
"semantic_scholar"
] | Mark Your LLM: Detecting the Misuse of Open-Source Large Language Models via Watermarking | As open-source large language models (LLMs) like Llama3 become more capable, it is crucial to develop watermarking techniques to detect their potential misuse. Existing watermarking methods either add watermarks during LLM inference, which is unsuitable for open-source LLMs, or primarily target classification LLMs rath... | [
"Yijie Xu",
"Aiwei Liu",
"Xuming Hu",
"Lijie Wen",
"Hui Xiong"
] | [
"cs.CL",
"cs.AI",
"cs.CR",
"cs.LG"
] | [
"Computer Science"
] | 2025-03-06T00:00:00 | https://arxiv.org/abs/2503.04636 | https://arxiv.org/pdf/2503.04636v2 | 2503.04636 | 10.48550/arXiv.2503.04636 | 16 | 1 | true | null | arXiv.org | 0.3076 |
60291a583b9767bcc2703b4c375f51a6d7ff0af813839449fd5ae9e359799474 | [
"arxiv",
"semantic_scholar"
] | Robust Data Watermarking in Language Models by Injecting Fictitious Knowledge | Data watermarking in language models injects traceable signals, such as specific token sequences or stylistic patterns, into copyrighted text, allowing copyright holders to track and verify training data ownership. Previous data watermarking techniques primarily focus on effective memorization during pretraining, while... | [
"Xinyue Cui",
"Johnny Tian-Zheng Wei",
"Swabha Swayamdipta",
"Robin Jia"
] | [
"cs.CR",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2025-03-06T00:00:00 | https://arxiv.org/abs/2503.04036 | https://arxiv.org/pdf/2503.04036v3 | 2503.04036 | 10.48550/arXiv.2503.04036 | 13 | 1 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.2865 |
3112ef497ced5cab3aa67aa702a57ff39884b986784b726a15e65c9fbb1e03b8 | [
"arxiv",
"semantic_scholar"
] | GaussianSeal: Rooting Adaptive Watermarks for 3D Gaussian Generation Model | With the advancement of AIGC technologies, the modalities generated by models have expanded from images and videos to 3D objects, leading to an increasing number of works focused on 3D Gaussian Splatting (3DGS) generative models. Existing research on copyright protection for generative models has primarily concentrated... | [
"Runyi Li",
"Xuanyu Zhang",
"Chuhan Tong",
"Zhipei Xu",
"Jian Zhang"
] | [
"cs.CV",
"eess.IV"
] | [
"Computer Science",
"Engineering"
] | 2025-03-01T00:00:00 | https://arxiv.org/abs/2503.00531 | https://arxiv.org/pdf/2503.00531v2 | 2503.00531 | 10.1007/s11633-025-1588-7 | 6 | 1 | false | null | Machine Intelligence Research | 0.2113 |
4bb7556861c870f69b4105cccc0f7f3ecbdf41732041a7bffb353b2a8a016973 | [
"arxiv",
"semantic_scholar"
] | Marking Code Without Breaking It: Code Watermarking for Detecting LLM-Generated Code | Identifying LLM-generated code through watermarking poses a challenge in preserving functional correctness. Previous methods rely on the assumption that watermarking high-entropy tokens effectively maintains output quality. Our analysis reveals a fundamental limitation of this assumption: syntax-critical tokens such as... | [
"Jungin Kim",
"Shinwoo Park",
"Yo-Sub Han"
] | [
"cs.CR",
"cs.AI"
] | [
"Computer Science"
] | 2025-02-26T00:00:00 | https://arxiv.org/abs/2502.18851 | https://arxiv.org/pdf/2502.18851v4 | 2502.18851 | 10.48550/arXiv.2502.18851 | 10 | 1 | true | https://github.com/inistory/STONE-watermarking | Conference of the European Chapter of the Association for Computational Linguistics | 0.2603 |
a13cf33b09a72ab14a076555593c120fb9fcedcba1fe62f540085ea38ba044fe | [
"arxiv",
"semantic_scholar"
] | Project Alexandria: Towards Freeing Scientific Knowledge from Copyright Burdens via LLMs | Paywalls, licenses and copyright rules often restrict the broad dissemination and reuse of scientific knowledge. We take the position that it is both legally and technically feasible to extract the scientific knowledge in scholarly texts. Current methods, like text embeddings, fail to reliably preserve factual content,... | [
"Christoph Schuhmann",
"Gollam Rabby",
"Ameya Prabhu",
"Tawsif Ahmed",
"Andreas Hochlehnert",
"Huu Nguyen",
"Nick Akinci",
"Ludwig Schmidt",
"Robert Kaczmarczyk",
"Sören Auer",
"Jenia Jitsev",
"Matthias Bethge"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2025-02-26T00:00:00 | https://arxiv.org/abs/2502.19413 | https://arxiv.org/pdf/2502.19413v2 | 2502.19413 | 10.48550/arXiv.2502.19413 | 0 | 0 | true | null | arXiv.org | 0.1098 |
911a29ad83e3015400988af2d4450ab33fc34b66c2485708c05277511180f707 | [
"arxiv",
"semantic_scholar"
] | Breaking Distortion-free Watermarks in Large Language Models | In recent years, LLM watermarking has emerged as an attractive safeguard against AI-generated content, with promising applications in many real-world domains. However, there are growing concerns that the current LLM watermarking schemes are vulnerable to expert adversaries wishing to reverse-engineer the watermarking m... | [
"Shayleen Reynolds",
"Hengzhi He",
"Dung Daniel T. Ngo",
"Saheed Obitayo",
"Niccolò Dalmasso",
"Guang Cheng",
"Vamsi K. Potluru",
"Manuela Veloso"
] | [
"cs.CR",
"cs.LG"
] | [
"Computer Science"
] | 2025-02-25T00:00:00 | https://arxiv.org/abs/2502.18608 | https://arxiv.org/pdf/2502.18608v2 | 2502.18608 | null | 0 | 0 | false | null | null | 0.0445 |
7c317b94e80ce106b766d8a72fe0b379d76bcf06a515e688b72bacde3591c254 | [
"arxiv",
"semantic_scholar"
] | KatFishNet: Detecting LLM-Generated Korean Text through Linguistic Feature Analysis | The rapid advancement of large language models (LLMs) increases the difficulty of distinguishing between human-written and LLM-generated text. Detecting LLM-generated text is crucial for upholding academic integrity, preventing plagiarism, protecting copyrights, and ensuring ethical research practices. Most prior studi... | [
"Shinwoo Park",
"Shubin Kim",
"Do-Kyung Kim",
"Yo-Sub Han"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-02-25T00:00:00 | https://arxiv.org/abs/2503.00032 | https://arxiv.org/pdf/2503.00032v5 | 2503.00032 | 10.18653/v1/2025.acl-long.1030 | 6 | 0 | true | https://github.com/Shinwoo-Park/detecting_llm_generated_korean_text_through_linguistic_analysis | Annual Meeting of the Association for Computational Linguistics | 0.2113 |
5d9e547dc733232d337c1572ef71f7990ad799a555b2e1cc54e966bde66130a3 | [
"arxiv",
"semantic_scholar"
] | Sarang at DEFACTIFY 4.0: Detecting AI-Generated Text Using Noised Data and an Ensemble of DeBERTa Models | This paper presents an effective approach to detect AI-generated text, developed for the Defactify 4.0 shared task at the fourth workshop on multimodal fact checking and hate speech detection. The task consists of two subtasks: Task-A, classifying whether a text is AI generated or human written, and Task-B, classifying... | [
"Avinash Trivedi",
"Sangeetha Sivanesan"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-02-24T00:00:00 | https://arxiv.org/abs/2502.16857 | https://arxiv.org/pdf/2502.16857v1 | 2502.16857 | 10.48550/arXiv.2502.16857 | 2 | 1 | false | null | arXiv.org | 0.1505 |
e9881846358212cd5eba830788dd58286b3afe3cd8039e07751c24c48541d819 | [
"arxiv",
"semantic_scholar"
] | Detecting Benchmark Contamination Through Watermarking | Benchmark contamination poses a significant challenge to the reliability of Large Language Models (LLMs) evaluations, as it is difficult to assert whether a model has been trained on a test set. We introduce a solution to this problem by watermarking benchmarks before their release. The embedding involves reformulating... | [
"Tom Sander",
"Pierre Fernandez",
"Saeed Mahloujifar",
"Alain Durmus",
"Chuan Guo"
] | [
"cs.CR",
"cs.AI"
] | [
"Computer Science"
] | 2025-02-24T00:00:00 | https://arxiv.org/abs/2502.17259 | https://arxiv.org/pdf/2502.17259v2 | 2502.17259 | 10.48550/arXiv.2502.17259 | 6 | 1 | false | null | arXiv.org | 0.2113 |
93561b61cd89722e701d8348cc33e068e383b8c040a2a6a711a502c4eebba46a | [
"arxiv",
"semantic_scholar"
] | Evaluating the Robustness and Accuracy of Text Watermarking Under Real-World Cross-Lingual Manipulations | We present a study to benchmark representative watermarking methods in cross-lingual settings. The current literature mainly focuses on the evaluation of watermarking methods for the English language. However, the literature for evaluating watermarking in cross-lingual settings is scarce. This results in overlooking im... | [
"Mansour Al Ghanim",
"Jiaqi Xue",
"Rochana Prih Hastuti",
"Mengxin Zheng",
"Yan Solihin",
"Qian Lou"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2025-02-23T00:00:00 | https://arxiv.org/abs/2502.16699 | https://arxiv.org/pdf/2502.16699v2 | 2502.16699 | 10.18653/v1/2025.findings-emnlp.390 | 5 | 0 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.1945 |
a568b9cf5592164168e15e729a43cb453cdd3886a661c817d34112a81a7cd8bd | [
"arxiv",
"semantic_scholar"
] | Can Large Vision-Language Models Detect Images Copyright Infringement from GenAI? | Generative AI models, renowned for their ability to synthesize high-quality content, have sparked growing concerns over the improper generation of copyright-protected material. While recent studies have proposed various approaches to address copyright issues, the capability of large vision-language models (LVLMs) to de... | [
"Qipan Xu",
"Zhenting Wang",
"Xiaoxiao He",
"Ligong Han",
"Ruixiang Tang"
] | [
"cs.CV",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2025-02-23T00:00:00 | https://arxiv.org/abs/2502.16618 | https://arxiv.org/pdf/2502.16618v1 | 2502.16618 | 10.48550/arXiv.2502.16618 | 6 | 0 | false | null | arXiv.org | 0.2113 |
9ddb812dede8ec2980229b40cd762adb6776698d58fbf38e73d3e587f043cf28 | [
"arxiv",
"semantic_scholar"
] | Deep Learning-based Dual Watermarking for Image Copyright Protection and Authentication | Advancements in digital technologies make it easy to modify the content of digital images. Hence, ensuring digital images integrity and authenticity is necessary to protect them against various attacks that manipulate them. We present a Deep Learning (DL) based dual invisible watermarking technique for performing sourc... | [
"Sudev Kumar Padhi",
"Archana Tiwari",
"Sk. Subidh Ali"
] | [
"cs.CR",
"cs.AI"
] | [
"Computer Science"
] | 2025-02-21T00:00:00 | https://arxiv.org/abs/2502.18501 | https://arxiv.org/pdf/2502.18501v1 | 2502.18501 | 10.1109/TAI.2024.3485519 | 15 | 3 | false | null | IEEE Transactions on Artificial Intelligence | 0.301 |
f6a795b0eef80fdfcde154c84382a6de5f1ce07209feb9d352eec4c881b1f2ed | [
"arxiv",
"semantic_scholar"
] | Can LLM Watermarks Robustly Prevent Unauthorized Knowledge Distillation? | The radioactive nature of Large Language Model (LLM) watermarking enables the detection of watermarks inherited by student models when trained on the outputs of watermarked teacher models, making it a promising tool for preventing unauthorized knowledge distillation. However, the robustness of watermark radioactivity a... | [
"Leyi Pan",
"Aiwei Liu",
"Shiyu Huang",
"Yijian Lu",
"Xuming Hu",
"Lijie Wen",
"Irwin King",
"Philip S. Yu"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-02-17T00:00:00 | https://arxiv.org/abs/2502.11598 | https://arxiv.org/pdf/2502.11598v2 | 2502.11598 | 10.48550/arXiv.2502.11598 | 11 | 2 | true | https://github.com/THU-BPM/Watermark-Radioactivity-Attack | Annual Meeting of the Association for Computational Linguistics | 0.2698 |
0bcc403ba94d25a81fab5c5b246e6a1f5580365057307b62129748fcc46b6fcd | [
"arxiv",
"semantic_scholar"
] | LLM-driven Knowledge Distillation for Dynamic Text-Attributed Graphs | Dynamic Text-Attributed Graphs (DyTAGs) have numerous real-world applications, e.g. social, collaboration, citation, communication, and review networks. In these networks, nodes and edges often contain text descriptions, and the graph structure can evolve over time. Future link prediction, edge classification, relation... | [
"Amit Roy",
"Ning Yan",
"Masood Mortazavi"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-02-15T00:00:00 | https://arxiv.org/abs/2502.10914 | https://arxiv.org/pdf/2502.10914v1 | 2502.10914 | 10.48550/arXiv.2502.10914 | 3 | 1 | false | null | arXiv.org | 0.1505 |
373b2bbbb09b8d5948716fad7ac271a38909364ec3f98327ea697ee80a589a34 | [
"arxiv",
"semantic_scholar"
] | Beyond English: Unveiling Multilingual Bias in LLM Copyright Compliance | Large Language Models (LLMs) have raised significant concerns regarding the fair use of copyright-protected content. While prior studies have examined the extent to which LLMs reproduce copyrighted materials, they have predominantly focused on English, neglecting multilingual dimensions of copyright protection. In this... | [
"Yupeng Chen",
"Xiaoyu Zhang",
"Yixian Huang",
"Qian Xie"
] | [
"cs.CY",
"cs.CL"
] | [
"Computer Science"
] | 2025-02-14T00:00:00 | https://arxiv.org/abs/2503.05713 | https://arxiv.org/pdf/2503.05713v1 | 2503.05713 | 10.48550/arXiv.2503.05713 | 2 | 1 | false | null | arXiv.org | 0.1505 |
18f91aa434b29d7231cf814397d83a93eaf01906a1441677b5d70c95afa5d81b | [
"arxiv",
"semantic_scholar"
] | Towards Watermarking of Open-Source LLMs | While watermarks for closed LLMs have matured and have been included in large-scale deployments, these methods are not applicable to open-source models, which allow users full control over the decoding process. This setting is understudied yet critical, given the rising performance of open-source models. In this work, ... | [
"Thibaud Gloaguen",
"Nikola Jovanović",
"Robin Staab",
"Martin Vechev"
] | [
"cs.CR",
"cs.LG"
] | [
"Computer Science"
] | 2025-02-14T00:00:00 | https://arxiv.org/abs/2502.10525 | https://arxiv.org/pdf/2502.10525v1 | 2502.10525 | 10.48550/arXiv.2502.10525 | 13 | 2 | true | null | arXiv.org | 0.2865 |
1168ed5125c8d155ff3765a6ca48519fdf9df49ce1553f316b68550d89e55250 | [
"arxiv",
"semantic_scholar"
] | GraphiT: Efficient Node Classification on Text-Attributed Graphs with Prompt Optimized LLMs | The application of large language models (LLMs) to graph data has attracted a lot of attention recently. LLMs allow us to use deep contextual embeddings from pretrained models in text-attributed graphs, where shallow embeddings are often used for the text attributes of nodes. However, it is still challenging to efficie... | [
"Shima Khoshraftar",
"Niaz Abedini",
"Amir Hajian"
] | [
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-02-14T00:00:00 | https://arxiv.org/abs/2502.10522 | https://arxiv.org/pdf/2502.10522v1 | 2502.10522 | 10.1145/3701716.3717805 | 6 | 0 | false | null | The Web Conference | 0.2113 |
26c9934ca47ad9f82ff75211420c34d5a253e2fa6d25459e40c15d5e75af1865 | [
"arxiv",
"semantic_scholar"
] | Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark | The development of large language models (LLMs) has raised concerns about potential misuse. One practical solution is to embed a watermark in the text, allowing ownership verification through watermark extraction. Existing methods primarily focus on defending against modification attacks, often neglecting other spoofin... | [
"Yuhang Cai",
"Yaofei Wang",
"Donghui Hu",
"Chen Gu"
] | [
"cs.CR",
"cs.AI"
] | [
"Computer Science"
] | 2025-02-12T00:00:00 | https://arxiv.org/abs/2502.08332 | https://arxiv.org/pdf/2502.08332v2 | 2502.08332 | 10.48550/arXiv.2502.08332 | 0 | 0 | false | null | arXiv.org | 0.055 |
386c02f986c25caee7b73a6cbfa12a71ff5c1122666b3117480dc4523fd477dd | [
"arxiv",
"semantic_scholar"
] | Image Watermarking of Generative Diffusion Models | Embedding watermarks into the output of generative models is essential for establishing copyright and verifiable ownership over the generated content. Emerging diffusion model watermarking methods either embed watermarks in the frequency domain or offer limited versatility of the watermark patterns in the image space, ... | [
"Yunzhuo Chen",
"Jordan Vice",
"Naveed Akhtar",
"Nur Al Hasan Haldar",
"Ajmal Mian"
] | [
"eess.IV",
"cs.CR"
] | [
"Computer Science",
"Engineering"
] | 2025-02-12T00:00:00 | https://arxiv.org/abs/2502.10465 | https://arxiv.org/pdf/2502.10465v1 | 2502.10465 | 10.48550/arXiv.2502.10465 | 4 | 0 | false | null | arXiv.org | 0.1747 |
3c3207a225f2517ce2db36c33dbcf1fffea4839ff2cfab15e6868c835dacce40 | [
"arxiv",
"semantic_scholar"
] | Toward Copyright Integrity and Verifiability via Multi-Bit Watermarking for Intelligent Transportation Systems | Intelligent transportation systems (ITS) use advanced technologies such as artificial intelligence to significantly improve traffic flow management efficiency, and promote the intelligent development of the transportation industry. However, if the data in ITS is attacked, such as tampering or forgery, it will endanger ... | [
"Yihao Wang",
"Lingxiao Li",
"Yifan Tang",
"Ru Zhang",
"Jianyi Liu"
] | [
"cs.CR",
"cs.CL"
] | [
"Computer Science"
] | 2025-02-08T00:00:00 | https://arxiv.org/abs/2502.05425 | https://arxiv.org/pdf/2502.05425v1 | 2502.05425 | 10.1109/TITS.2025.3535932 | 8 | 0 | false | null | IEEE Transactions on Intelligent Transportation Systems, 07 February 2025 | 0.2386 |
c773caadfd3413931be7eb72d4b28ba5a9f34c3be5b226de1ef3155738974606 | [
"arxiv",
"semantic_scholar"
] | XAttnMark: Learning Robust Audio Watermarking with Cross-Attention | The rapid proliferation of generative audio synthesis and editing technologies has raised serious concerns about copyright infringement, data provenance, and the spread of misinformation via deepfake audio. Watermarking offers a proactive solution by embedding imperceptible yet identifiable and traceable signals into a... | [
"Yixin Liu",
"Lie Lu",
"Jihui Jin",
"Lichao Sun",
"Andrea Fanelli"
] | [
"cs.SD",
"cs.AI",
"cs.CR",
"cs.LG",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2025-02-06T00:00:00 | https://arxiv.org/abs/2502.04230 | https://arxiv.org/pdf/2502.04230v3 | 2502.04230 | 10.48550/arXiv.2502.04230 | 10 | 0 | false | null | International Conference on Machine Learning | 0.2603 |
241a3d5c10d5a90a8697cc5531181eea04148e2fd1f9a4e6758a051f926129cc | [
"arxiv",
"semantic_scholar"
] | DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large Language Models | As large language models (LLMs) grow more powerful, concerns over copyright infringement of LLM-generated texts have intensified. LLM watermarking has been proposed to trace unauthorized redistribution or resale of generated content by embedding identifiers within the text. Existing approaches primarily rely on one-bit... | [
"Qihao Lin",
"Chen Tang",
"Lan zhang",
"Junyang zhang",
"Xiangyang Li"
] | [
"cs.CR",
"cs.AI"
] | [
"Computer Science"
] | 2025-02-04T00:00:00 | https://arxiv.org/abs/2502.05213 | https://arxiv.org/pdf/2502.05213v2 | 2502.05213 | 10.48550/arXiv.2502.05213 | 0 | 0 | false | null | arXiv.org | 0.0458 |
81552158272fe9ae25c2f7a0c5550286d33224f88cc8b3307a0ea34de64fd63f | [
"arxiv",
"semantic_scholar"
] | Watermarking across Modalities for Content Tracing and Generative AI | Watermarking embeds information into digital content like images, audio, or text, imperceptible to humans but robustly detectable by specific algorithms. This technology has important applications in many challenges of the industry such as content moderation, tracing AI-generated content, and monitoring the usage of AI... | [
"Pierre Fernandez"
] | [
"cs.CR",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-02-04T00:00:00 | https://arxiv.org/abs/2502.05215 | https://arxiv.org/pdf/2502.05215v1 | 2502.05215 | 10.48550/arXiv.2502.05215 | 2 | 0 | false | null | arXiv.org | 0.1193 |
ea3d1d1db971575665d74d9c090a66547116d25fb6c9519fc5972e97c1134041 | [
"arxiv",
"semantic_scholar"
] | BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks | The rapid growth of Large Language Models (LLMs) raises concerns about distinguishing AI-generated text from human content. Existing watermarking techniques, like \kgw, struggle with low watermark strength and stringent false-positive requirements. Our analysis reveals that current methods rely on coarse estimates of n... | [
"Zhuang Li",
"Qiuping Yi",
"Zongcheng Ji",
"Yijian Lu",
"Yanqi Li",
"Keyang Xiao",
"Hongliang Liang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-01-21T00:00:00 | https://arxiv.org/abs/2501.12174 | https://arxiv.org/pdf/2501.12174v6 | 2501.12174 | 10.48550/arXiv.2501.12174 | 2 | 0 | false | null | Neurocomputing | 0.1193 |
b077655bc0d6a824070feaa58ee24489d22fe1e739f2af171fa7e4d260a69e73 | [
"arxiv",
"semantic_scholar"
] | GaussMark: A Practical Approach for Structural Watermarking of Language Models | Recent advances in Large Language Models (LLMs) have led to significant improvements in natural language processing tasks, but their ability to generate human-quality text raises significant ethical and operational concerns in settings where it is important to recognize whether or not a given text was generated by a hu... | [
"Adam Block",
"Ayush Sekhari",
"Alexander Rakhlin"
] | [
"cs.CR",
"cs.AI",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2025-01-17T00:00:00 | https://arxiv.org/abs/2501.13941 | https://arxiv.org/pdf/2501.13941v1 | 2501.13941 | 10.48550/arXiv.2501.13941 | 11 | 4 | false | null | International Conference on Machine Learning | 0.3495 |
c41ca6eec673179131c842cb9dec4522e1c6e9ddd2fe69e02681fe702cb92aaa | [
"arxiv",
"semantic_scholar"
] | SoccerSynth-Detection: A Synthetic Dataset for Soccer Player Detection | In soccer video analysis, player detection is essential for identifying key events and reconstructing tactical positions. The presence of numerous players and frequent occlusions, combined with copyright restrictions, severely restricts the availability of datasets, leaving limited options such as SoccerNet-Tracking an... | [
"Haobin Qin",
"Calvin Yeung",
"Rikuhei Umemoto",
"Keisuke Fujii"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-01-16T00:00:00 | https://arxiv.org/abs/2501.09281 | https://arxiv.org/pdf/2501.09281v2 | 2501.09281 | 10.48550/arXiv.2501.09281 | 2 | 0 | true | https://github.com/open-starlab/SoccerSynth-Detection | arXiv.org | 0.1193 |
4abe1b5bddc920b0b2e831d7b30b6c002b91523e3fe97079e760404ec991b74e | [
"arxiv",
"semantic_scholar"
] | Leveraging Explainable AI for LLM Text Attribution: Differentiating Human-Written and Multiple LLMs-Generated Text | The development of Generative AI Large Language Models (LLMs) raised the alarm regarding identifying content produced through generative AI or humans. In one case, issues arise when students heavily rely on such tools in a manner that can affect the development of their writing or coding skills. Other issues of plagiar... | [
"Ayat Najjar",
"Huthaifa I. Ashqar",
"Omar Darwish",
"Eman Hammad"
] | [
"cs.CL",
"cs.CY"
] | [
"Computer Science"
] | 2025-01-06T00:00:00 | https://arxiv.org/abs/2501.03212 | https://arxiv.org/pdf/2501.03212v1 | 2501.03212 | 10.48550/arXiv.2501.03212 | 16 | 1 | false | null | null | 0.3076 |
15a5a6c1cf4f44e6bc79a5e73a0ff1d8089f0fc8ce9e4ab1f458168dab088d6c | [
"arxiv",
"semantic_scholar"
] | RTLMarker: Protecting LLM-Generated RTL Copyright via a Hardware Watermarking Framework | Recent advances of large language models in the field of Verilog generation have raised several ethical and security concerns, such as code copyright protection and dissemination of malicious code. Researchers have employed watermarking techniques to identify codes generated by large language models. However, the exist... | [
"Kun Wang",
"Kaiyan Chang",
"Mengdi Wang",
"Xinqi Zou",
"Haobo Xu",
"Yinhe Han",
"Ying Wang"
] | [
"cs.CR",
"cs.AI"
] | [
"Computer Science"
] | 2025-01-05T00:00:00 | https://arxiv.org/abs/2501.02446 | https://arxiv.org/pdf/2501.02446v1 | 2501.02446 | 10.1145/3658617.3697774 | 7 | 0 | false | null | Asia and South Pacific Design Automation Conference | 0.2258 |
aead6aab7e4c405369e3ef22c27834e95cdeb86fd259b351add10691e00f5357 | [
"arxiv",
"semantic_scholar"
] | A Training-free Method for LLM Text Attribution | Verifying the provenance of content is crucial to the functioning of many organizations, e.g., educational institutions, social media platforms, and firms. This problem is becoming increasingly challenging as text generated by Large Language Models (LLMs) becomes almost indistinguishable from human-generated content. I... | [
"Tara Radvand",
"Mojtaba Abdolmaleki",
"Mohamed Mostagir",
"Ambuj Tewari"
] | [
"stat.ML",
"cs.AI",
"cs.CL",
"cs.IT",
"cs.LG"
] | [
"Mathematics",
"Computer Science"
] | 2025-01-04T00:00:00 | https://arxiv.org/abs/2501.02406 | https://arxiv.org/pdf/2501.02406v5 | 2501.02406 | null | 3 | 0 | true | https://github.com/TaraRadvand74/llm-text-detection | null | 0.1505 |
c28eab89802e1a44dcd98907d5f83788f763afd3dfec603071c4b7256d80ebdf | [
"arxiv",
"semantic_scholar"
] | TextSleuth: Towards Explainable Tampered Text Detection | Recently, tampered text detection has attracted increasing attention due to its essential role in information security. Although existing methods can detect the tampered text region, the interpretation of such detection remains unclear, making the prediction unreliable. To address this problem, we propose to explain th... | [
"Chenfan Qu",
"Jian Liu",
"Haoxing Chen",
"Baihan Yu",
"Jingjing Liu",
"Weiqiang Wang",
"Lianwen Jin"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-12-19T00:00:00 | https://arxiv.org/abs/2412.14816 | https://arxiv.org/pdf/2412.14816v3 | 2412.14816 | null | 6 | 1 | true | null | null | 0.2113 |
2d8fef12b3919ed6092b257d8df7135b12503d4dd35951a3d22e681708684b36 | [
"arxiv",
"semantic_scholar"
] | SuperMark: Robust and Training-free Image Watermarking via Diffusion-based Super-Resolution | In today's digital landscape, the blending of AI-generated and authentic content has underscored the need for copyright protection and content authentication. Watermarking has become a vital tool to address these challenges, safeguarding both generated and real content. Effective watermarking methods must withstand var... | [
"Runyi Hu",
"Jie Zhang",
"Yiming Li",
"Jiwei Li",
"Qing Guo",
"Han Qiu",
"Tianwei Zhang"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-12-13T00:00:00 | https://arxiv.org/abs/2412.10049 | https://arxiv.org/pdf/2412.10049v1 | 2412.10049 | 10.48550/arXiv.2412.10049 | 5 | 0 | false | null | arXiv.org | 0.1945 |
ccf8cf4dbb70b13a4f2c8017a68be6c44e4993aad5d989f13bed5d24d505a1d5 | [
"arxiv",
"semantic_scholar"
] | Watermarking Training Data of Music Generation Models | Generative Artificial Intelligence (Gen-AI) models are increasingly used to produce content across domains, including text, images, and audio. While these models represent a major technical breakthrough, they gain their generative capabilities from being trained on enormous amounts of human-generated content, which oft... | [
"Pascal Epple",
"Igor Shilov",
"Bozhidar Stevanoski",
"Yves-Alexandre de Montjoye"
] | [
"cs.LG",
"cs.SD",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2024-12-11T00:00:00 | https://arxiv.org/abs/2412.08549 | https://arxiv.org/pdf/2412.08549v2 | 2412.08549 | 10.48550/arXiv.2412.08549 | 6 | 1 | false | null | arXiv.org | 0.2113 |
1840ea2a47d093a0e938bdec90d4ecf8bc5e7bd756ab440d7f040cb0b10f1d89 | [
"arxiv",
"semantic_scholar"
] | Exploring Memorization and Copyright Violation in Frontier LLMs: A Study of the New York Times v. OpenAI 2023 Lawsuit | Copyright infringement in frontier LLMs has received much attention recently due to the New York Times v. OpenAI lawsuit, filed in December 2023. The New York Times claims that GPT-4 has infringed its copyrights by reproducing articles for use in LLM training and by memorizing the inputs, thereby publicly displaying th... | [
"Joshua Freeman",
"Chloe Rippe",
"Edoardo Debenedetti",
"Maksym Andriushchenko"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-12-09T00:00:00 | https://arxiv.org/abs/2412.06370 | https://arxiv.org/pdf/2412.06370v1 | 2412.06370 | 10.48550/arXiv.2412.06370 | 26 | 0 | false | null | arXiv.org | 0.3578 |
7ea19eb71bd75879f361c946886a4512f1a3d8e52e544f493f31df31a6fa4a40 | [
"arxiv",
"semantic_scholar"
] | WATER-GS: Toward Copyright Protection for 3D Gaussian Splatting via Universal Watermarking | 3D Gaussian Splatting (3DGS) has emerged as a pivotal technique for 3D scene representation, providing rapid rendering speeds and high fidelity. As 3DGS gains prominence, safeguarding its intellectual property becomes increasingly crucial since 3DGS could be used to imitate unauthorized scene creations and raise copyri... | [
"Yuqi Tan",
"Xiang Liu",
"Shuzhao Xie",
"Bin Chen",
"Shu-Tao Xia",
"Zhi Wang"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2024-12-07T00:00:00 | https://arxiv.org/abs/2412.05695 | https://arxiv.org/pdf/2412.05695v1 | 2412.05695 | 10.48550/arXiv.2412.05695 | 6 | 1 | false | null | arXiv.org | 0.2113 |
d01a29cbef6068f760d536dc024f881080198c15930f76599ab59090935aac62 | [
"arxiv",
"semantic_scholar"
] | Robust Multi-bit Text Watermark with LLM-based Paraphrasers | We propose an imperceptible multi-bit text watermark embedded by paraphrasing with LLMs. We fine-tune a pair of LLM paraphrasers that are designed to behave differently so that their paraphrasing difference reflected in the text semantics can be identified by a trained decoder. To embed our multi-bit watermark, we use ... | [
"Xiaojun Xu",
"Jinghan Jia",
"Yuanshun Yao",
"Yang Liu",
"Hang Li"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2024-12-04T00:00:00 | https://arxiv.org/abs/2412.03123 | https://arxiv.org/pdf/2412.03123v2 | 2412.03123 | 10.48550/arXiv.2412.03123 | 9 | 0 | true | https://github.com/xiaojunxu/multi-bit-text-watermark | International Conference on Machine Learning | 0.25 |
f116e4e278366d4da1cb655023c7ef8d6a2f79772877aa706d143f46a8d5c295 | [
"arxiv",
"semantic_scholar"
] | CredID: Credible Multi-Bit Watermark for Large Language Models Identification | Large Language Models (LLMs) are widely used in complex natural language processing tasks but raise privacy and security concerns due to the lack of identity recognition. This paper proposes a multi-party credible watermarking framework (CredID) involving a trusted third party (TTP) and multiple LLM vendors to address ... | [
"Haoyu Jiang",
"Xuhong Wang",
"Ping Yi",
"Shanzhe Lei",
"Yilun Lin"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2024-12-04T00:00:00 | https://arxiv.org/abs/2412.03107 | https://arxiv.org/pdf/2412.03107v2 | 2412.03107 | 10.48550/arXiv.2412.03107 | 5 | 0 | true | null | IEEE International Conference on Acoustics, Speech, and Signal Processing | 0.1945 |
24af201045f41c5d93bd0a596d58a1cc4d45652ef613570b3929b158160ff451 | [
"arxiv",
"semantic_scholar"
] | The Efficacy of Transfer-based No-box Attacks on Image Watermarking: A Pragmatic Analysis | Watermarking approaches are widely used to identify if images being circulated are authentic or AI-generated. Determining the robustness of image watermarking methods in the ``no-box'' setting, where the attacker is assumed to have no knowledge about the watermarking model, is an interesting problem. Our main finding i... | [
"Qilong Wu",
"Varun Chandrasekaran"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2024-12-03T00:00:00 | https://arxiv.org/abs/2412.02576 | https://arxiv.org/pdf/2412.02576v1 | 2412.02576 | 10.48550/arXiv.2412.02576 | 1 | 0 | true | https://github.com/Ardor-Wu/transfer} | arXiv.org | 0.0753 |
53828638016e5dbef9cbe6efde9397bd0c818af4970507104dc16e13be75de14 | [
"arxiv",
"semantic_scholar"
] | Ensemble Watermarks for Large Language Models | As large language models (LLMs) reach human-like fluency, reliably distinguishing AI-generated text from human authorship becomes increasingly difficult. While watermarks already exist for LLMs, they often lack flexibility and struggle with attacks such as paraphrasing. To address these issues, we propose a multi-featu... | [
"Georg Niess",
"Roman Kern"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-11-29T00:00:00 | https://arxiv.org/abs/2411.19563 | https://arxiv.org/pdf/2411.19563v2 | 2411.19563 | 10.48550/arXiv.2411.19563 | 5 | 0 | true | http://github.com/CommodoreEU/ensemble-watermark | Annual Meeting of the Association for Computational Linguistics | 0.1945 |
1a7cebd34771c979bf09cdce75b77b1140510ff52e25b8aa31810b03992c41b9 | [
"arxiv",
"semantic_scholar"
] | Robust Detection of Watermarks for Large Language Models Under Human Edits | Watermarking has offered an effective approach to distinguishing text generated by large language models (LLMs) from human-written text. However, the pervasive presence of human edits on LLM-generated text dilutes watermark signals, thereby significantly degrading detection performance of existing methods. In this pape... | [
"Xiang Li",
"Feng Ruan",
"Huiyuan Wang",
"Qi Long",
"Weijie J. Su"
] | [
"stat.ME",
"cs.CL",
"cs.LG",
"math.ST",
"stat.ML"
] | [
"Computer Science",
"Medicine",
"Mathematics"
] | 2024-11-21T00:00:00 | https://arxiv.org/abs/2411.13868 | https://arxiv.org/pdf/2411.13868v3 | 2411.13868 | 10.48550/arXiv.2411.13868 | 24 | 1 | true | null | Journal of The Royal Statistical Society Series B-statistical Methodology | 0.3495 |
23e043fea6b37d3c816d270160a0f449501196b18f580e9c36b92e9876d7757d | [
"arxiv",
"semantic_scholar"
] | Watermark under Fire: A Robustness Evaluation of LLM Watermarking | Various watermarking methods (``watermarkers'') have been proposed to identify LLM-generated texts; yet, due to the lack of unified evaluation platforms, many critical questions remain under-explored: i) What are the strengths/limitations of various watermarkers, especially their attack robustness? ii) How do various d... | [
"Jiacheng Liang",
"Zian Wang",
"Lauren Hong",
"Shouling Ji",
"Ting Wang"
] | [
"cs.CR",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2024-11-20T00:00:00 | https://arxiv.org/abs/2411.13425 | https://arxiv.org/pdf/2411.13425v4 | 2411.13425 | 10.18653/v1/2025.findings-emnlp.1148 | 4 | 1 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.1747 |
451b36e92b78b141aef773a64a37b9e2e9ff70c2baa36f6d4964ab6ac1d353d9 | [
"arxiv",
"semantic_scholar"
] | CopyrightMeter: Revisiting Copyright Protection in Text-to-image Models | Text-to-image diffusion models have emerged as powerful tools for generating high-quality images from textual descriptions. However, their increasing popularity has raised significant copyright concerns, as these models can be misused to reproduce copyrighted content without authorization. In response, recent studies h... | [
"Naen Xu",
"Changjiang Li",
"Tianyu Du",
"Minxi Li",
"Wenjie Luo",
"Jiacheng Liang",
"Yuyuan Li",
"Xuhong Zhang",
"Meng Han",
"Jianwei Yin",
"Ting Wang"
] | [
"cs.CR",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2024-11-20T00:00:00 | https://arxiv.org/abs/2411.13144 | https://arxiv.org/pdf/2411.13144v1 | 2411.13144 | 10.48550/arXiv.2411.13144 | 8 | 1 | false | null | arXiv.org | 0.2386 |
1a4ecbd64ecada11614ce84bcb068f77b10a5966deb9835daebd82dd14850218 | [
"arxiv",
"semantic_scholar"
] | Data Watermarking for Sequential Recommender Systems | In the era of large foundation models, data has become a crucial component in building high-performance AI systems. As the demand for high-quality and large-scale data continues to rise, data copyright protection is attracting increasing attention. In this work, we explore the problem of data watermarking for sequentia... | [
"Sixiao Zhang",
"Cheng Long",
"Wei Yuan",
"Hongxu Chen",
"Hongzhi Yin"
] | [
"cs.IR"
] | [
"Computer Science"
] | 2024-11-20T00:00:00 | https://arxiv.org/abs/2411.12989 | https://arxiv.org/pdf/2411.12989v2 | 2411.12989 | 10.1145/3711896.3736903 | 3 | 0 | false | null | Knowledge Discovery and Data Mining | 0.1505 |
3ba660dcd57553fd69825ea3bffd6c10e0b3a33275372d0c19144ffe25e7c4f4 | [
"arxiv",
"semantic_scholar"
] | Watermarking Visual Concepts for Diffusion Models | The personalization techniques of diffusion models succeed in generating images with specific concepts. This ability also poses great threats to copyright protection and network security since malicious users can generate unauthorized content and disinformation relevant to a target concept. Model watermarking is an eff... | [
"Liangqi Lei",
"Keke Gai",
"Jing Yu",
"Liehuang Zhu",
"Qi Wu"
] | [
"cs.CR",
"cs.AI",
"cs.MM"
] | [
"Computer Science"
] | 2024-11-18T00:00:00 | https://arxiv.org/abs/2411.11688 | https://arxiv.org/pdf/2411.11688v3 | 2411.11688 | null | 2 | 0 | false | null | null | 0.1193 |
8209655e9d9387b1afbb726d5acb4a9b9a92b022cb376558d6e5f85720db6f4f | [
"arxiv",
"semantic_scholar"
] | SEFD: Semantic-Enhanced Framework for Detecting LLM-Generated Text | The widespread adoption of large language models (LLMs) has created an urgent need for robust tools to detect LLM-generated text, especially in light of \textit{paraphrasing} techniques that often evade existing detection methods. To address this challenge, we present a novel semantic-enhanced framework for detecting L... | [
"Weiqing He",
"Bojian Hou",
"Tianqi Shang",
"Davoud Ataee Tarzanagh",
"Qi Long",
"Li Shen"
] | [
"cs.CL",
"cs.AI",
"cs.IR"
] | [
"Medicine",
"Computer Science"
] | 2024-11-17T00:00:00 | https://arxiv.org/abs/2411.12764 | https://arxiv.org/pdf/2411.12764v1 | 2411.12764 | 10.1109/BigData62323.2024.10825110 | 5 | 0 | false | null | BigData Congress [Services Society] | 0.1945 |
378a1e6ff04e3c0e0653ee369ba4e19657b4d2d06d995b86a2fd65ff7acfb042 | [
"arxiv",
"semantic_scholar"
] | Your Semantic-Independent Watermark is Fragile: A Semantic Perturbation Attack against EaaS Watermark | Embedding-as-a-Service (EaaS) has emerged as a successful business pattern but faces significant challenges related to various forms of copyright infringement, particularly, the API misuse and model extraction attacks. Various studies have proposed backdoor-based watermarking schemes to protect the copyright of EaaS se... | [
"Zekun Fei",
"Biao Yi",
"Jianing Geng",
"Ruiqi He",
"Lihai Nie",
"Zheli Liu"
] | [
"cs.CR",
"cs.AI"
] | [
"Computer Science"
] | 2024-11-14T00:00:00 | https://arxiv.org/abs/2411.09359 | https://arxiv.org/pdf/2411.09359v2 | 2411.09359 | 10.18653/v1/2025.findings-emnlp.192 | 0 | 0 | true | https://github.com/Zk4-ps/EaaS-Embedding-Watermark | Conference on Empirical Methods in Natural Language Processing | 0 |
d98c49f7fe5a336a6cff05b230671d5dfd46e0979353df4e93db83c671d27502 | [
"arxiv",
"semantic_scholar"
] | Robust Detection of LLM-Generated Text: A Comparative Analysis | The ability of large language models to generate complex texts allows them to be widely integrated into many aspects of life, and their output can quickly fill all network resources. As the impact of LLMs grows, it becomes increasingly important to develop powerful detectors for the generated text. This detector is ess... | [
"Yongye Su",
"Yuqing Wu"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-11-09T00:00:00 | https://arxiv.org/abs/2411.06248 | https://arxiv.org/pdf/2411.06248v1 | 2411.06248 | 10.48550/arXiv.2411.06248 | 4 | 0 | true | null | arXiv.org | 0.1747 |
33d4c26b7ead665975e752eb35a307065ab425637b87f1747f0b24c4ed316b83 | [
"arxiv",
"semantic_scholar"
] | Revisiting the Robustness of Watermarking to Paraphrasing Attacks | Amidst rising concerns about the internet being proliferated with content generated from language models (LMs), watermarking is seen as a principled way to certify whether text was generated from a model. Many recent watermarking techniques slightly modify the output probabilities of LMs to embed a signal in the genera... | [
"Saksham Rastogi",
"Danish Pruthi"
] | [
"cs.CR",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2024-11-08T00:00:00 | https://arxiv.org/abs/2411.05277 | https://arxiv.org/pdf/2411.05277v1 | 2411.05277 | 10.18653/v1/2024.emnlp-main.1005 | 22 | 2 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.3404 |
7a9b02f9329799bfcfa7869feff8dc4e92f1edf286d2e7bface99dd7d3fdb0da | [
"arxiv",
"semantic_scholar"
] | Understanding the Effects of Human-written Paraphrases in LLM-generated Text Detection | Natural Language Generation has been rapidly developing with the advent of large language models (LLMs). While their usage has sparked significant attention from the general public, it is important for readers to be aware when a piece of text is LLM-generated. This has brought about the need for building models that en... | [
"Hiu Ting Lau",
"Arkaitz Zubiaga"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-11-06T00:00:00 | https://arxiv.org/abs/2411.03806 | https://arxiv.org/pdf/2411.03806v1 | 2411.03806 | 10.48550/arXiv.2411.03806 | 16 | 2 | false | null | Natural Language Processing Journal | 0.3076 |
4af686c2bad9f5ae2ab6c665534d06ceb802d975f92858def6b0ca1f4aa6f940 | [
"arxiv",
"semantic_scholar"
] | Do LLMs Know to Respect Copyright Notice? | Prior study shows that LLMs sometimes generate content that violates copyright. In this paper, we study another important yet underexplored problem, i.e., will LLMs respect copyright information in user input, and behave accordingly? The research problem is critical, as a negative answer would imply that LLMs will beco... | [
"Jialiang Xu",
"Shenglan Li",
"Zhaozhuo Xu",
"Denghui Zhang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-11-02T00:00:00 | https://arxiv.org/abs/2411.01136 | https://arxiv.org/pdf/2411.01136v1 | 2411.01136 | 10.18653/v1/2024.emnlp-main.1147 | 19 | 2 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.3253 |
96bc13fe37d8ccdcb3b9a460115814ddae0b4c5d15d041ff8f9b7a7fbc6208d9 | [
"arxiv",
"semantic_scholar"
] | $B^4$: A Black-Box Scrubbing Attack on LLM Watermarks | Watermarking has emerged as a prominent technique for LLM-generated content detection by embedding imperceptible patterns. Despite supreme performance, its robustness against adversarial attacks remains underexplored. Previous work typically considers a grey-box attack setting, where the specific type of watermark is a... | [
"Baizhou Huang",
"Xiao Pu",
"Xiaojun Wan"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-11-02T00:00:00 | https://arxiv.org/abs/2411.01222 | https://arxiv.org/pdf/2411.01222v3 | 2411.01222 | 10.48550/arXiv.2411.01222 | 8 | 0 | false | null | North American Chapter of the Association for Computational Linguistics | 0.2386 |
caf92666bf3bf978659e96a333f550339927037dc7a1ccb1af9519094ee7a0b3 | [
"arxiv",
"semantic_scholar"
] | Segmenting Watermarked Texts From Language Models | Watermarking is a technique that involves embedding nearly unnoticeable statistical signals within generated content to help trace its source. This work focuses on a scenario where an untrusted third-party user sends prompts to a trusted language model (LLM) provider, who then generates a text from their LLM with a wat... | [
"Xingchi Li",
"Guanxun Li",
"Xianyang Zhang"
] | [
"cs.LG",
"cs.MM",
"cs.NE",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2024-10-28T00:00:00 | https://arxiv.org/abs/2410.20670 | https://arxiv.org/pdf/2410.20670v1 | 2410.20670 | 10.48550/arXiv.2410.20670 | 5 | 1 | true | https://github.com/doccstat/llm-watermark-cpd | Neural Information Processing Systems | 0.1945 |
3196c77fc468f3fe66137565653b381d5293c72613dc9edc779f6fb92beae698 | [
"arxiv",
"semantic_scholar"
] | Robust and Minimally Invasive Watermarking for EaaS | Embeddings as a Service (EaaS) is emerging as a crucial role in AI applications. Unfortunately, EaaS is vulnerable to model extraction attacks, highlighting the urgent need for copyright protection. Although some preliminary works propose applying embedding watermarks to protect EaaS, recent research reveals that these... | [
"Zongqi Wang",
"Baoyuan Wu",
"Jingyuan Deng",
"Yujiu Yang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-10-23T00:00:00 | https://arxiv.org/abs/2410.17552 | https://arxiv.org/pdf/2410.17552v3 | 2410.17552 | 10.18653/v1/2025.findings-acl.112 | 4 | 2 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.2386 |
e56895c52b1a8a1e5630f8cf8178b7f169507e541b17c238dba2d9d01f3ba52e | [
"arxiv",
"semantic_scholar"
] | Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement | The rapid development of large language models (LLMs), like ChatGPT, has resulted in the widespread presence of LLM-generated content on social media platforms, raising concerns about misinformation, data biases, and privacy violations, which can undermine trust in online discourse. While detecting LLM-generated conten... | [
"Zihao Cheng",
"Li Zhou",
"Feng Jiang",
"Benyou Wang",
"Haizhou Li"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-10-18T00:00:00 | https://arxiv.org/abs/2410.14259 | https://arxiv.org/pdf/2410.14259v2 | 2410.14259 | 10.1145/3696410.3714770 | 20 | 3 | false | null | The Web Conference | 0.3306 |
de6ad8fa8071a939d68193c5837348647615ca5df28f8baa31bc3f36b214bf33 | [
"arxiv",
"semantic_scholar"
] | Beyond Dataset Watermarking: Model-Level Copyright Protection for Code Summarization Models | Code Summarization Model (CSM) has been widely used in code production, such as online and web programming for PHP and Javascript. CSMs are essential tools in code production, enhancing software development efficiency and driving innovation in automated code analysis. However, CSMs face risks of exploitation by unautho... | [
"Jiale Zhang",
"Haoxuan Li",
"Di Wu",
"Xiaobing Sun",
"Qinghua Lu",
"Guodong Long"
] | [
"cs.CR",
"cs.CY"
] | [
"Computer Science"
] | 2024-10-18T00:00:00 | https://arxiv.org/abs/2410.14102 | https://arxiv.org/pdf/2410.14102v2 | 2410.14102 | 10.1145/3696410.3714641 | 2 | 0 | false | null | The Web Conference | 0.1193 |
fcb58e9adcc533059d6987fd802b7b30a7a90b6296184a6a6f5d63d3f9632925 | [
"arxiv",
"semantic_scholar"
] | Which LLMs are Difficult to Detect? A Detailed Analysis of Potential Factors Contributing to Difficulties in LLM Text Detection | As LLMs increase in accessibility, LLM-generated texts have proliferated across several fields, such as scientific, academic, and creative writing. However, LLMs are not created equally; they may have different architectures and training datasets. Thus, some LLMs may be more challenging to detect than others. Using two... | [
"Shantanu Thorat",
"Tianbao Yang"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2024-10-18T00:00:00 | https://arxiv.org/abs/2410.14875 | https://arxiv.org/pdf/2410.14875v2 | 2410.14875 | 10.48550/arXiv.2410.14875 | 2 | 0 | false | null | arXiv.org | 0.1193 |
0b8ff528e2bd027add6fcb9416cae093cf6509fc33975df413ba7485154c0063 | [
"arxiv",
"semantic_scholar"
] | FreqMark: Frequency-Based Watermark for Sentence-Level Detection of LLM-Generated Text | The increasing use of Large Language Models (LLMs) for generating highly coherent and contextually relevant text introduces new risks, including misuse for unethical purposes such as disinformation or academic dishonesty. To address these challenges, we propose FreqMark, a novel watermarking technique that embeds detec... | [
"Zhenyu Xu",
"Kun Zhang",
"Victor S. Sheng"
] | [
"cs.CL",
"cs.CR",
"cs.LG"
] | [
"Computer Science"
] | 2024-10-09T00:00:00 | https://arxiv.org/abs/2410.10876 | https://arxiv.org/pdf/2410.10876v1 | 2410.10876 | 10.48550/arXiv.2410.10876 | 7 | 0 | false | null | arXiv.org | 0.2258 |
7fd098ad791ff0cb34e5532eced836a72df927e5e3701557d4b80d8b1d90e1e4 | [
"arxiv",
"semantic_scholar"
] | WAPITI: A Watermark for Finetuned Open-Source LLMs | Watermarking of large language models (LLMs) generation embeds an imperceptible statistical pattern within texts, making it algorithmically detectable. Watermarking is a promising method for addressing potential harm and biases from LLMs, as it enables traceability, accountability, and detection of manipulated content,... | [
"Lingjie Chen",
"Ruizhong Qiu",
"Siyu Yuan",
"Zhining Liu",
"Tianxin Wei",
"Hyunsik Yoo",
"Zhichen Zeng",
"Deqing Yang",
"Hanghang Tong"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2024-10-09T00:00:00 | https://arxiv.org/abs/2410.06467 | https://arxiv.org/pdf/2410.06467v2 | 2410.06467 | 10.48550/arXiv.2410.06467 | 16 | 0 | true | null | arXiv.org | 0.3076 |
c38ca3e5137164af2a3a54d55c286de2210939eacd7516bc8d414ca06bd87ae0 | [
"arxiv",
"semantic_scholar"
] | Signal Watermark on Large Language Models | As Large Language Models (LLMs) become increasingly sophisticated, they raise significant security concerns, including the creation of fake news and academic misuse. Most detectors for identifying model-generated text are limited by their reliance on variance in perplexity and burstiness, and they require substantial c... | [
"Zhenyu Xu",
"Victor S. Sheng"
] | [
"cs.CR",
"cs.LG"
] | [
"Computer Science"
] | 2024-10-09T00:00:00 | https://arxiv.org/abs/2410.06545 | https://arxiv.org/pdf/2410.06545v1 | 2410.06545 | 10.48550/arXiv.2410.06545 | 2 | 0 | false | null | arXiv.org | 0.1193 |
2c7398787a71ec499741dc20b0be7bc7fa990efc0741f7bf46d1d14939143c48 | [
"arxiv",
"semantic_scholar"
] | Inner-Probe: Discovering Copyright-related Data Generation in LLM Architecture | Large Language Models (LLMs) utilize extensive knowledge databases and show powerful text generation ability. However, their reliance on high-quality copyrighted datasets raises concerns about copyright infringements in generated texts. Current research often employs prompt engineering or semantic classifiers to identi... | [
"Qichao Ma",
"Rui-Jie Zhu",
"Peiye Liu",
"Renye Yan",
"Fahong Zhang",
"Ling Liang",
"Meng Li",
"Zhaofei Yu",
"Zongwei Wang",
"Yimao Cai",
"Tiejun Huang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-10-06T00:00:00 | https://arxiv.org/abs/2410.04454 | https://arxiv.org/pdf/2410.04454v3 | 2410.04454 | 10.1109/TAI.2025.3645710 | 2 | 0 | false | null | IEEE Transactions on Artificial Intelligence | 0.1193 |
ac3c4f7a6103ca6a68181eb9c94b7323862a8208f49f5d4e9f2f2e7a5b933957 | [
"arxiv",
"semantic_scholar"
] | Can Watermarked LLMs be Identified by Users via Crafted Prompts? | Text watermarking for Large Language Models (LLMs) has made significant progress in detecting LLM outputs and preventing misuse. Current watermarking techniques offer high detectability, minimal impact on text quality, and robustness to text editing. However, current researches lack investigation into the imperceptibil... | [
"Aiwei Liu",
"Sheng Guan",
"Yiming Liu",
"Leyi Pan",
"Yifei Zhang",
"Liancheng Fang",
"Lijie Wen",
"Philip S. Yu",
"Xuming Hu"
] | [
"cs.CR",
"cs.CL"
] | [
"Computer Science"
] | 2024-10-04T00:00:00 | https://arxiv.org/abs/2410.03168 | https://arxiv.org/pdf/2410.03168v3 | 2410.03168 | 10.48550/arXiv.2410.03168 | 17 | 2 | true | https://github.com/THU-BPM/Watermarked_LLM_Identification | International Conference on Learning Representations | 0.3138 |
0ad4a69022ec2812b4e64ed7f8836d2ba560124dee3d83dd7b811676229162ba | [
"arxiv",
"semantic_scholar"
] | Efficiently Identifying Watermarked Segments in Mixed-Source Texts | Text watermarks in large language models (LLMs) are increasingly used to detect synthetic text, mitigating misuse cases like fake news and academic dishonesty. While existing watermarking detection techniques primarily focus on classifying entire documents as watermarked or not, they often neglect the common scenario o... | [
"Xuandong Zhao",
"Chenwen Liao",
"Yu-Xiang Wang",
"Lei Li"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-10-04T00:00:00 | https://arxiv.org/abs/2410.03600 | https://arxiv.org/pdf/2410.03600v2 | 2410.03600 | 10.48550/arXiv.2410.03600 | 3 | 1 | true | https://github.com/XuandongZhao/llm-watermark-location | Annual Meeting of the Association for Computational Linguistics | 0.1505 |
e8583aad248ef8cd6df28ddfa2456f4caec77f7cc2f539a7a400f81efd439259 | [
"arxiv",
"semantic_scholar"
] | Theoretically Grounded Framework for LLM Watermarking: A Distribution-Adaptive Approach | Watermarking has emerged as a crucial method to distinguish AI-generated text from human-created text. Current watermarking approaches often lack formal optimality guarantees or address the scheme and detector design separately. In this paper, we introduce a novel, unified theoretical framework for watermarking Large L... | [
"Haiyun He",
"Yepeng Liu",
"Ziqiao Wang",
"Yongyi Mao",
"Yuheng Bu"
] | [
"cs.CR",
"cs.IT",
"cs.LG"
] | [
"Computer Science",
"Mathematics"
] | 2024-10-03T00:00:00 | https://arxiv.org/abs/2410.02890 | https://arxiv.org/pdf/2410.02890v5 | 2410.02890 | null | 18 | 2 | true | https://github.com/yepengliu/DAWA | null | 0.3197 |
f994e192099d399ee49aa389834d43f32b9f6ec1a5d13c318879f864c67c21d5 | [
"arxiv",
"semantic_scholar"
] | Optimizing Adaptive Attacks against Watermarks for Language Models | Large Language Models (LLMs) can be misused to spread unwanted content at scale. Content watermarking deters misuse by hiding messages in content, enabling its detection using a secret watermarking key. Robustness is a core security property, stating that evading detection requires (significant) degradation of the cont... | [
"Abdulrahman Diaa",
"Toluwani Aremu",
"Nils Lukas"
] | [
"cs.CR",
"cs.AI"
] | [
"Computer Science"
] | 2024-10-03T00:00:00 | https://arxiv.org/abs/2410.02440 | https://arxiv.org/pdf/2410.02440v2 | 2410.02440 | null | 10 | 1 | true | https://github.com/nilslukas/ada-wm-evasion | International Conference on Machine Learning | 0.2603 |
bce7d1e6974ad83bcf51843195e8373817a1627a0dc3d8e0bccce46f68b000cc | [
"arxiv",
"semantic_scholar"
] | Bridging Speech and Text: Enhancing ASR with Pinyin-to-Character Pre-training in LLMs | The integration of large language models (LLMs) with pre-trained speech models has opened up new avenues in automatic speech recognition (ASR). While LLMs excel in multimodal understanding tasks, effectively leveraging their capabilities for ASR remains a significant challenge. This paper presents a novel training appr... | [
"Yang Yuhang",
"Peng Yizhou",
"Eng Siong Chng",
"Xionghu Zhong"
] | [
"cs.CL",
"cs.SD",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2024-09-24T00:00:00 | https://arxiv.org/abs/2409.16005 | https://arxiv.org/pdf/2409.16005v1 | 2409.16005 | 10.1109/ISCSLP63861.2024.10800477 | 3 | 1 | false | null | International Symposium on Chinese Spoken Language Processing | 0.1505 |
aad8807283bcec7100428c5dace1d30936c6feb906cc4a500c05dfd54357a13b | [
"arxiv",
"semantic_scholar"
] | Towards Effective User Attribution for Latent Diffusion Models via Watermark-Informed Blending | Rapid advancements in multimodal large language models have enabled the creation of hyper-realistic images from textual descriptions. However, these advancements also raise significant concerns about unauthorized use, which hinders their broader distribution. Traditional watermarking methods often require complex integ... | [
"Yongyang Pan",
"Xiaohong Liu",
"Siqi Luo",
"Yi Xin",
"Xiao Guo",
"Xiaoming Liu",
"Xiongkuo Min",
"Guangtao Zhai"
] | [
"cs.MM",
"cs.CR",
"cs.CV",
"eess.IV"
] | [
"Computer Science",
"Engineering"
] | 2024-09-17T00:00:00 | https://arxiv.org/abs/2409.10958 | https://arxiv.org/pdf/2409.10958v2 | 2409.10958 | 10.48550/arXiv.2409.10958 | 5 | 0 | false | null | arXiv.org | 0.1945 |
8ad98cf9f14c8dd25c75f155e576467d2c433b849b5eec799fb57a2303b461c4 | [
"arxiv",
"semantic_scholar"
] | PersonaMark: Personalized LLM watermarking for model protection and user attribution | The rapid advancement of customized Large Language Models (LLMs) offers considerable convenience. However, it also intensifies concerns regarding the protection of copyright/confidential information. With the extensive adoption of private LLMs, safeguarding model copyright and ensuring data privacy have become critical... | [
"Yuehan Zhang",
"Peizhuo Lv",
"Yinpeng Liu",
"Yongqiang Ma",
"Wei Lu",
"Xiaofeng Wang",
"Xiaozhong Liu",
"Jiawei Liu"
] | [
"cs.CR",
"cs.CL"
] | [
"Computer Science"
] | 2024-09-15T00:00:00 | https://arxiv.org/abs/2409.09739 | https://arxiv.org/pdf/2409.09739v2 | 2409.09739 | 10.48550/arXiv.2409.09739 | 5 | 0 | false | null | arXiv.org | 0.1945 |
434ceb9a5ee181309ded69c1e3dbd6418c3e7a1ad305d222a15b2110c32a7a9d | [
"arxiv",
"semantic_scholar"
] | Protecting Copyright of Medical Pre-trained Language Models: Training-Free Backdoor Model Watermarking | With the advancement of intelligent healthcare, medical pre-trained language models (Med-PLMs) have emerged and demonstrated significant effectiveness in downstream medical tasks. While these models are valuable assets, they are vulnerable to misuse and theft, requiring copyright protection. However, existing watermark... | [
"Cong Kong",
"Rui Xu",
"Weixi Chen",
"Jiawei Chen",
"Zhaoxia Yin"
] | [
"cs.LG",
"cs.AI",
"cs.CR"
] | [
"Computer Science"
] | 2024-09-14T00:00:00 | https://arxiv.org/abs/2409.10570 | https://arxiv.org/pdf/2409.10570v2 | 2409.10570 | 10.1145/3746027.3755548 | 1 | 0 | false | null | ACM Multimedia | 0.0753 |
bb2adb3f9e944e68d7424b1fa2240d75272ff7c167e89d172a20692084ea24b5 | [
"arxiv",
"semantic_scholar"
] | Generating API Parameter Security Rules with LLM for API Misuse Detection | In this paper, we present a new framework, named GPTAid, for automatic APSRs generation by analyzing API source code with LLM and detecting API misuse caused by incorrect parameter use. To validate the correctness of the LLM-generated APSRs, we propose an execution feedback-checking approach based on the observation th... | [
"Jinghua Liu",
"Yi Yang",
"Kai Chen",
"Miaoqian Lin"
] | [
"cs.CR",
"cs.SE"
] | [
"Computer Science"
] | 2024-09-14T00:00:00 | https://arxiv.org/abs/2409.09288 | https://arxiv.org/pdf/2409.09288v2 | 2409.09288 | 10.14722/ndss.2025.23465 | 16 | 0 | false | null | Network and Distributed System Security Symposium | 0.3076 |
fad7fbdda2f1004eef64369639ea42086a4f75a540ffd80776436e8a7d6d559c | [
"arxiv",
"semantic_scholar"
] | WaterSeeker: Pioneering Efficient Detection of Watermarked Segments in Large Documents | Watermarking algorithms for large language models (LLMs) have attained high accuracy in detecting LLM-generated text. However, existing methods primarily focus on distinguishing fully watermarked text from non-watermarked text, overlooking real-world scenarios where LLMs generate only small sections within large docume... | [
"Leyi Pan",
"Aiwei Liu",
"Yijian Lu",
"Zitian Gao",
"Yichen Di",
"Shiyu Huang",
"Lijie Wen",
"Irwin King",
"Philip S. Yu"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-09-08T00:00:00 | https://arxiv.org/abs/2409.05112 | https://arxiv.org/pdf/2409.05112v6 | 2409.05112 | 10.18653/v1/2025.findings-naacl.156 | 5 | 1 | true | https://github.com/THU-BPM/WaterSeeker | North American Chapter of the Association for Computational Linguistics | 0.1945 |
f4944228cd0a01b15781cd1c5bcd5d6b8c127bd4202a4a0cb179152b0f7e1a8e | [
"arxiv",
"semantic_scholar"
] | Agentic Copyright Watermarking against Adversarial Evidence Forgery with Purification-Agnostic Curriculum Proxy Learning | With the proliferation of AI agents in various domains, protecting the ownership of AI models has become crucial due to the significant investment in their development. Unauthorized use and illegal distribution of these models pose serious threats to intellectual property, necessitating effective copyright protection m... | [
"Erjin Bao",
"Ching-Chun Chang",
"Hanrui Wang",
"Isao Echizen"
] | [
"cs.CV",
"cs.CR"
] | [
"Computer Science"
] | 2024-09-03T00:00:00 | https://arxiv.org/abs/2409.01541 | https://arxiv.org/pdf/2409.01541v2 | 2409.01541 | 10.1109/ICASSP49660.2025.10889676 | 0 | 0 | false | null | IEEE International Conference on Acoustics, Speech, and Signal Processing | 0 |
bbcbbf05ba799c87890f21b4cdf17dacd007b7a522878cd69d048dcf20ebc252 | [
"arxiv",
"semantic_scholar"
] | RLCP: A Reinforcement Learning-based Copyright Protection Method for Text-to-Image Diffusion Model | The increasing sophistication of text-to-image generative models has led to complex challenges in defining and enforcing copyright infringement criteria and protection. Existing methods, such as watermarking and dataset deduplication, fail to provide comprehensive solutions due to the lack of standardized metrics and t... | [
"Zhuan Shi",
"Jing Yan",
"Xiaoli Tang",
"Lingjuan Lyu",
"Boi Faltings"
] | [
"cs.CY",
"cs.AI",
"cs.CR"
] | [
"Computer Science"
] | 2024-08-29T00:00:00 | https://arxiv.org/abs/2408.16634 | https://arxiv.org/pdf/2408.16634v3 | 2408.16634 | 10.1109/ICME59968.2025.11210136 | 4 | 0 | false | null | IEEE International Conference on Multimedia and Expo | 0.1747 |
dc9b27bd0e2330f90512057ff4a7be6394b4121beed53148b3e829c9a06fd850 | [
"arxiv",
"semantic_scholar"
] | Watermarking Techniques for Large Language Models: A Survey | With the rapid advancement and extensive application of artificial intelligence technology, large language models (LLMs) are extensively used to enhance production, creativity, learning, and work efficiency across various domains. However, the abuse of LLMs also poses potential harm to human society, such as intellectu... | [
"Yuqing Liang",
"Jiancheng Xiao",
"Wensheng Gan",
"Philip S. Yu"
] | [
"cs.CR",
"cs.AI"
] | [
"Computer Science"
] | 2024-08-26T00:00:00 | https://arxiv.org/abs/2409.00089 | https://arxiv.org/pdf/2409.00089v1 | 2409.00089 | 10.1007/s10462-025-11474-6 | 31 | 1 | false | null | Artificial Intelligence Review | 0.3763 |
fe26000d46f6e01a99726870640909867c4242645cb720f7d388c008f34369b8 | [
"arxiv",
"semantic_scholar"
] | Systematic Evaluation of LLM-as-a-Judge in LLM Alignment Tasks: Explainable Metrics and Diverse Prompt Templates | LLM-as-a-Judge has been widely applied to evaluate and compare different LLM alignmnet approaches (e.g., RLHF and DPO). However, concerns regarding its reliability have emerged, due to LLM judges' biases and inconsistent decision-making. Previous research has developed evaluation frameworks to assess reliability of LLM... | [
"Hui Wei",
"Shenghua He",
"Tian Xia",
"Fei Liu",
"Andy Wong",
"Jingyang Lin",
"Mei Han"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-08-23T00:00:00 | https://arxiv.org/abs/2408.13006 | https://arxiv.org/pdf/2408.13006v2 | 2408.13006 | 10.48550/arXiv.2408.13006 | 81 | 6 | true | null | arXiv.org | 0.4785 |
515ff9cc49af6f74cfe3f5d797eca6900a44896d19774d861072260aa61c6754 | [
"arxiv",
"semantic_scholar"
] | Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges | Accurate attribution of authorship is crucial for maintaining the integrity of digital content, improving forensic investigations, and mitigating the risks of misinformation and plagiarism. Addressing the imperative need for proper authorship attribution is essential to uphold the credibility and accountability of auth... | [
"Baixiang Huang",
"Canyu Chen",
"Kai Shu"
] | [
"cs.CY"
] | [
"Computer Science",
"Medicine"
] | 2024-08-16T00:00:00 | https://arxiv.org/abs/2408.08946 | https://arxiv.org/pdf/2408.08946v3 | 2408.08946 | 10.1145/3715073.3715076 | 72 | 1 | false | null | SIGKDD Explorations | 0.4658 |
0591003e9abc546c2fefc7f344c6e996f6cd4caf6a1316636cb5368c863975ca | [
"arxiv",
"semantic_scholar"
] | LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection | The ease of access to large language models (LLMs) has enabled a widespread of machine-generated texts, and now it is often hard to tell whether a piece of text was human-written or machine-generated. This raises concerns about potential misuse, particularly within educational and academic domains. Thus, it is importan... | [
"Mervat Abassy",
"Kareem Elozeiri",
"Alexander Aziz",
"Minh Ngoc Ta",
"Raj Vardhan Tomar",
"Bimarsha Adhikari",
"Saad El Dine Ahmed",
"Yuxia Wang",
"Osama Mohammed Afzal",
"Zhuohan Xie",
"Jonibek Mansurov",
"Ekaterina Artemova",
"Vladislav Mikhailov",
"Rui Xing",
"Jiahui Geng",
"Hasan ... | [
"cs.CL"
] | [
"Computer Science"
] | 2024-08-08T00:00:00 | https://arxiv.org/abs/2408.04284 | https://arxiv.org/pdf/2408.04284v3 | 2408.04284 | 10.48550/arXiv.2408.04284 | 44 | 1 | true | https://github.com/mbzuai-nlp/LLM-DetectAIve | Conference on Empirical Methods in Natural Language Processing | 0.4133 |
d691f14829429afe0a29b1b0508b380b77c8f2f9e9db807862370dcea28a479a | [
"arxiv",
"semantic_scholar"
] | Learning to Rewrite: Generalized LLM-Generated Text Detection | Large language models (LLMs) present significant risks when used to generate non-factual content and spread disinformation at scale. Detecting such LLM-generated content is crucial, yet current detectors often struggle to generalize in open-world contexts. We introduce Learning2Rewrite, a novel framework for detecting ... | [
"Ran Li",
"Wei Hao",
"Weiliang Zhao",
"Junfeng Yang",
"Chengzhi Mao"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2024-08-08T00:00:00 | https://arxiv.org/abs/2408.04237 | https://arxiv.org/pdf/2408.04237v2 | 2408.04237 | 10.48550/arXiv.2408.04237 | 15 | 1 | false | null | arXiv.org | 0.301 |
a258520a0fd0c5c8916989b72cfe55019f961f7a301771436a3717e4f41f21ff | [
"arxiv",
"semantic_scholar"
] | Robustness of Watermarking on Text-to-Image Diffusion Models | Watermarking has become one of promising techniques to not only aid in identifying AI-generated images but also serve as a deterrent against the unethical use of these models. However, the robustness of watermarking techniques has not been extensively studied recently. In this paper, we investigate the robustness of ge... | [
"Xiaodong Wu",
"Xiangman Li",
"Jianbing Ni"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2024-08-04T00:00:00 | https://arxiv.org/abs/2408.02035 | https://arxiv.org/pdf/2408.02035v2 | 2408.02035 | 10.48550/arXiv.2408.02035 | 1 | 0 | false | null | arXiv.org | 0.0753 |
3642ad86a75528b313dfc68eb886caa45cbb2a03a08418452028513f8fd1d04d | [
"arxiv",
"semantic_scholar"
] | Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data? | Large Language Models (LLMs) have demonstrated impressive capabilities in generating diverse and contextually rich text. However, concerns regarding copyright infringement arise as LLMs may inadvertently produce copyrighted material. In this paper, we first investigate the effectiveness of watermarking LLMs as a deterr... | [
"Michael-Andrei Panaitescu-Liess",
"Zora Che",
"Bang An",
"Yuancheng Xu",
"Pankayaraj Pathmanathan",
"Souradip Chakraborty",
"Sicheng Zhu",
"Tom Goldstein",
"Furong Huang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-07-24T00:00:00 | https://arxiv.org/abs/2407.17417 | https://arxiv.org/pdf/2407.17417v3 | 2407.17417 | 10.48550/arXiv.2407.17417 | 23 | 0 | true | https://github.com/michael-panaitescu/watermark_copyright_aaai25 | AAAI Conference on Artificial Intelligence | 0.3451 |
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