Data-Security: Watermarking Research & WaterBench Application

A comprehensive watermarking research platform combining algorithm benchmarking and an interactive web application for exploring and testing image watermarking techniques.

Figure 1: Our benchmarking pipeline follows a three step process given an image: (1) encode a message as an invisible watermark in the image using one of the methods discussed in \autoref{sec:related_work}, (2) compute image quality metrics on the watermarked image to assess its imperceptibility, (3) attack the watermarked image with a transformation (e.g. cropping the image) (4) use the methods decoder to extract the message from the attacked watermarked image and (5) compute watermark detection metrics.

The research paper and presentation slides are available in docs/ (Paper | Presentation).

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Overview

This repository serves two purposes:

1. Benchmarking - Systematic evaluation of watermarking algorithms against various attacks
2. WaterBench - An interactive web application for demonstrating watermarking capabilities and cryptographic ownership claims

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Project Structure

Data-Security/
├── benchmark/           # Benchmarking scripts and results
│   ├── pipeline.py      # Main benchmark execution engine
│   ├── visualization.py # Results visualization and plotting
│   ├── vendor/          # Watermarking algorithm implementations
│   ├── results/         # Benchmark result JSON files
│   └── figures/         # Generated plots and visualizations
│
├── watermark-app/       # React frontend application
│   └── src/
│       ├── pages/       # Landing, Challenge, Benchmark, Ownership
│       └── components/  # Reusable UI components
│
└── watermark-backend/   # FastAPI backend server
    └── src/
        ├── server.py    # FastAPI application entry
        ├── api_routes.py# REST API endpoints
        └── vendor/      # Algorithm implementations

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Watermarking Methods

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Benchmark Results

The benchmark evaluates all methods against multiple attack categories:

• Distortion: Brightness, Contrast, Blur, Noise,
• Geometric: Rotation, Scaling, Cropping
• Compression: JPEG quality compression

Robustness Comparison

Spider plot comparing algorithm robustness across different attack types

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Getting Started

Prerequisites

• Python 3.10+
• Node.js 18+
• PyTorch (with MPS/CUDA support recommended)

Benchmark Setup

cd benchmark

# Install dependencies
pip install torch torchvision diffusers transformers accelerate \
    invisible-watermark datasets numpy opencv-python kornia pillow

# Run benchmarks
python pipeline.py

# Generate visualizations
python visualization.py

Backend Setup

cd watermark-backend

# Install dependencies
pip install -r requirements.txt

# Start server
python src/server.py

The API will be available at http://localhost:8000. API docs at /docs.

Frontend Setup

cd watermark-app

# Install dependencies
npm install

# Start development server
npm run dev

The app will be available at http://localhost:5174.

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WaterBench Application

Features

Challenge Mode - Interactive attack simulation

• Select image and watermarking algorithm
• Apply real-time attacks (blur, noise, rotation, compression)
• Extract and verify watermark survival

Ownership Claims - Cryptographic proof of authorship

• RSA-2048 key generation in browser
• Sign ownership claims with private key
• Verify claims with public key infrastructure

Benchmark View - Algorithm comparison dashboard

• Imperceptibility metrics (PSNR, SSIM)
• Performance metrics (embed/extract time)
• Robustness scores per attack type

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API Endpoints

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Technologies

Frontend: React 19, Vite, Tailwind CSS, WebCrypto API

Backend: FastAPI, PyTorch, Pillow, Cryptography

Benchmarking: PyTorch, Diffusers, HuggingFace Datasets, Kornia

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License

This project is for research and educational purposes. Check the LICENSE for more information.