WaterVIB: Learning Minimal Sufficient Watermark Representations via Variational Information Bottleneck
arXiv:2602.21508v1 Announce Type: new Abstract: Robust watermarking is critical for intellectual property protection, whereas existing methods face a severe vulnerability against regeneration-based AIGC attacks. We identify that existing methods fail because they entangle the watermark with high-frequency cover texture, which is susceptible to being rewritten during generative purification. To address this, we propose WaterVIB, a theoretically grounded framework that reformulates the encoder as an information sieve via the Variational Information Bottleneck. Instead of overfitting to fragile cover details, our approach forces the model to learn a Minimal Sufficient Statistic of the message. This effectively filters out redundant cover nuances prone to generative shifts, retaining only the essential signal invariant to regeneration. We theoretically prove that optimizing this bottleneck is a necessary condition for robustness against distribution-shifting attacks. Extensive experiments
arXiv:2602.21508v1 Announce Type: new Abstract: Robust watermarking is critical for intellectual property protection, whereas existing methods face a severe vulnerability against regeneration-based AIGC attacks. We identify that existing methods fail because they entangle the watermark with high-frequency cover texture, which is susceptible to being rewritten during generative purification. To address this, we propose WaterVIB, a theoretically grounded framework that reformulates the encoder as an information sieve via the Variational Information Bottleneck. Instead of overfitting to fragile cover details, our approach forces the model to learn a Minimal Sufficient Statistic of the message. This effectively filters out redundant cover nuances prone to generative shifts, retaining only the essential signal invariant to regeneration. We theoretically prove that optimizing this bottleneck is a necessary condition for robustness against distribution-shifting attacks. Extensive experiments demonstrate that WaterVIB significantly outperforms state-of-the-art methods, achieving superior zero-shot resilience against unknown diffusion-based editing.
Executive Summary
The article proposes WaterVIB, a novel framework for robust watermarking that utilizes the Variational Information Bottleneck to learn minimal sufficient watermark representations. This approach effectively filters out redundant cover nuances, retaining only the essential signal invariant to regeneration, and achieves superior zero-shot resilience against unknown diffusion-based editing. The framework is theoretically grounded and has been proven to be a necessary condition for robustness against distribution-shifting attacks. Extensive experiments demonstrate that WaterVIB outperforms state-of-the-art methods.
Key Points
- ▸ WaterVIB utilizes the Variational Information Bottleneck to learn minimal sufficient watermark representations
- ▸ The approach filters out redundant cover nuances, retaining only the essential signal invariant to regeneration
- ▸ WaterVIB achieves superior zero-shot resilience against unknown diffusion-based editing
Merits
Robustness against distribution-shifting attacks
WaterVIB's approach ensures that the watermark is not entangled with high-frequency cover texture, making it more resistant to regeneration-based AIGC attacks
Theoretically grounded framework
The framework is based on a solid theoretical foundation, providing a necessary condition for robustness against distribution-shifting attacks
Demerits
Limited generalizability
The framework's performance may be limited to specific types of watermarks or cover images, and may not generalize well to other scenarios
Expert Commentary
The proposed WaterVIB framework represents a significant advancement in the field of robust watermarking, as it addresses the key vulnerability of existing methods to regeneration-based AIGC attacks. By utilizing the Variational Information Bottleneck to learn minimal sufficient watermark representations, WaterVIB effectively filters out redundant cover nuances and retains only the essential signal invariant to regeneration. This approach has important implications for intellectual property protection and the development of more robust machine learning models. However, further research is needed to fully explore the framework's limitations and potential applications.
Recommendations
- ✓ Further research is needed to explore the framework's limitations and potential applications
- ✓ The development of more robust watermarking systems using WaterVIB's approach should be prioritized to protect intellectual property rights
