Robust Watermarks Leak: Channel-Aware Feature Extraction Enables Adversarial Watermark Manipulation

2025-02-10

AI Summary

This work uncovers a fundamental paradox between robustness and stealth in digital watermarking: existing methods enhance resilience against realistic distortions (e.g., JPEG compression, noise) by introducing detectable structural redundancies, thereby leaking watermark information. To address this, we propose the first single-image attack framework that requires neither access to the detector nor additional training data. Our method employs channel-aware multi-channel feature extraction and adversarial perturbation optimization guided by a pre-trained vision model, enabling end-to-end watermark forgery and detector evasion. Crucially, we are the first to reinterpret structural redundancy—traditionally introduced for robustness—as an exploitable vulnerability, enabling successful attacks from a single watermarked image alone. Experiments demonstrate a 60% improvement in detection evasion success rate and a 51% increase in forgery accuracy, while preserving high visual fidelity.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

Abstract

Watermarking plays a key role in the provenance and detection of AI-generated content. While existing methods prioritize robustness against real-world distortions (e.g., JPEG compression and noise addition), we reveal a fundamental tradeoff: such robust watermarks inherently improve the redundancy of detectable patterns encoded into images, creating exploitable information leakage. To leverage this, we propose an attack framework that extracts leakage of watermark patterns through multi-channel feature learning using a pre-trained vision model. Unlike prior works requiring massive data or detector access, our method achieves both forgery and detection evasion with a single watermarked image. Extensive experiments demonstrate that our method achieves a 60% success rate gain in detection evasion and 51% improvement in forgery accuracy compared to state-of-the-art methods while maintaining visual fidelity. Our work exposes the robustness-stealthiness paradox: current"robust"watermarks sacrifice security for distortion resistance, providing insights for future watermark design.

Problem

• Robust watermarks leak information
• Extracting watermark patterns via multi-channel features
• Balancing robustness and security in watermarking

Innovation

• Multi-channel feature learning
• Single image watermark attack
• Robustness-stealthiness paradox