README.md

metadata

language:
  - en
tags:
  - image-watermarking
  - onnx
  - computer-vision

Watermark Anything Model (WAM) - ONNX

This repository contains the ONNX conversion of the Watermark Anything Model (WAM). To ensure optimal performance and flexibility, the monolithic PyTorch model has been decomposed into three distinct ONNX computational graphs.

Model Components

The watermarking pipeline is divided into three .onnx files, ensuring that image scaling and heavy computations (like Just Noticeable Difference - JND attenuation) can be performed optimally.

1. embedder.onnx

   • Inputs:
     • image: Float32 [batch_size, 3, 256, 256] (Normalized image)
     • message: Float32 [batch_size, nbits] (Binary payload to embed)
   • Outputs:
     • watermark: Float32 [batch_size, 3, 256, 256] (Watermark signal/deltas)

2. blender.onnx

   • Inputs:
     • imgs: Float32 [batch_size, 3, height, width] (Original normalized image at any resolution)
     • deltas: Float32 [batch_size, 3, height, width] (Watermark signal resized to original image dimensions)
   • Outputs:
     • watermarked_image: Float32 [batch_size, 3, height, width] (Final normalized image with JND applied)

3. extractor.onnx

   • Inputs:
     • image: Float32 [batch_size, 3, 256, 256] (Watermarked image, resized and normalized)
   • Outputs:
     • mask: Float32 [batch_size, 1 + nbits, 256, 256] (Detected watermark mask and extracted message bits)

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Inference Guide (Python)

You will need onnxruntime, numpy, torch, torchvision, and Pillow.

1. Installation

pip install onnxruntime numpy torch torchvision pillow

2. End-to-End Example

Below is a complete Python script to load an image, embed a watermark, save the result, and extract the watermark mask.

import numpy as np
import onnxruntime as ort
import torch
from torchvision import transforms
from PIL import Image

# ---------------------------------------------------------
# 1. Initialization
# ---------------------------------------------------------
# Load ONNX Runtime sessions
embedder_sess = ort.InferenceSession("embedder.onnx")
blender_sess = ort.InferenceSession("blender.onnx")
extractor_sess = ort.InferenceSession("extractor.onnx")

# Define ImageNet normalization stats
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]

normalize = transforms.Normalize(mean=mean, std=std)
unnormalize = transforms.Normalize(
    mean=[-m/s for m, s in zip(mean, std)], 
    std=[1/s for s in std]
)
resize_256 = transforms.Resize((256, 256), antialias=True)
to_tensor = transforms.ToTensor()

# Configure watermark bits (0 means only localizer mask, no payload)
nbits = 0 

# ---------------------------------------------------------
# 2. Image Preprocessing
# ---------------------------------------------------------
img_path = "input_image.jpg"
pil_img = Image.open(img_path).convert("RGB")
img_tensor = to_tensor(pil_img).unsqueeze(0)  # [1, 3, H, W]

# Normalize for the model
img_normalized = normalize(img_tensor)
original_h, original_w = img_tensor.shape[2], img_tensor.shape[3]

# ---------------------------------------------------------
# 3. Embedding the Watermark Signal
# ---------------------------------------------------------
img_resized = resize_256(img_normalized)
msg = np.random.randint(0, 2, (1, nbits)).astype(np.float32)

embedder_inputs = {
    'image': img_resized.numpy(),
    'message': msg
}
# Output is [1, 3, 256, 256]
watermark_deltas = embedder_sess.run(None, embedder_inputs)[0] 

# ---------------------------------------------------------
# 4. Blending & JND Attenuation
# ---------------------------------------------------------
# Resize the watermark signal back to the original image resolution
inverse_resize = transforms.Resize((original_h, original_w), antialias=True)
watermark_deltas_full = inverse_resize(torch.from_numpy(watermark_deltas)).numpy()

blender_inputs = {
    'imgs': img_normalized.numpy(),
    'deltas': watermark_deltas_full
}
# Output is [1, 3, H, W] normalized
watermarked_img_normalized = blender_sess.run(None, blender_inputs)[0]

# ---------------------------------------------------------
# 5. Postprocessing and Saving
# ---------------------------------------------------------
watermarked_img_tensor = torch.from_numpy(watermarked_img_normalized)
watermarked_img_unnorm = unnormalize(watermarked_img_tensor)
watermarked_img_clamped = torch.clamp(watermarked_img_unnorm, 0, 1)

output_img = transforms.ToPILImage()(watermarked_img_clamped.squeeze(0))
output_img.save("watermarked_image.png")
print("✅ Watermarked image saved!")

# ---------------------------------------------------------
# 6. Extraction Detection
# ---------------------------------------------------------
# Resize the watermarked image to 256x256 before feeding into the extractor
extractor_input = resize_256(watermarked_img_tensor).numpy()

extractor_inputs = {
    'image': extractor_input
}
extracted_mask = extractor_sess.run(None, extractor_inputs)[0]

print(f"✅ Extracted mask shape: {extracted_mask.shape}")

Performance & Optimization

Replacing the native monolithic implementation with these ONNX models offers massive portability, meaning it can be run seamlessly on edge devices using ONNX Runtime (CPU, CUDA, TensorRT, CoreML).

Note: The blender.onnx relies on dynamic axes for image resolution, allowing you to avoid resizing your pristine original images. The embedder and extractor graphs operate strictly at 256x256 to align with their training resolutions.

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license: mit