Decoupling Defense Strategies for Robust Image Watermarking
Jiahui Chen, Zehang Deng, Zeyu Zhang, Chaoyang Li, Lianchen Jia, Lifeng Sun

TL;DR
This paper introduces AdvMark, a two-stage fine-tuning framework that decouples defense strategies to enhance robustness of deep learning-based image watermarking against various attacks while maintaining high image quality.
Contribution
The paper proposes a novel two-stage decoupled approach for robust image watermarking, addressing adversarial and distortion attacks separately to improve robustness and visual quality.
Findings
Achieves up to 29% accuracy improvement against distortion attacks.
Achieves up to 33% accuracy improvement against regeneration attacks.
Achieves up to 46% accuracy improvement against adversarial attacks.
Abstract
Deep learning-based image watermarking, while robust against conventional distortions, remains vulnerable to advanced adversarial and regeneration attacks. Conventional countermeasures, which jointly optimize the encoder and decoder via a noise layer, face 2 inevitable challenges: (1) decrease of clean accuracy due to decoder adversarial training and (2) limited robustness due to simultaneous training of all three advanced attacks. To overcome these issues, we propose AdvMark, a novel two-stage fine-tuning framework that decouples the defense strategies. In stage 1, we address adversarial vulnerability via a tailored adversarial training paradigm that primarily fine-tunes the encoder while only conditionally updating the decoder. This approach learns to move the image into a non-attackable region, rather than modifying the decision boundary, thus preserving clean accuracy. In stage 2,…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Advanced Steganography and Watermarking Techniques · Digital Media Forensic Detection
