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Pengcheng Sun

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Computer Vision eess.SY Information Theory math.IT Systems and Control

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preprint2026arXiv

CSGuard: Toward Forgery-Resistant Watermarking in Diffusion Models via Compressed Sensing Constraint

Latent-based diffusion model watermarking embeds watermarks into generated images' latent space to enable content attribution, offering a training-free solution for intellectual property protection and digital forensics. However, these methods exhibit a critical vulnerability to the forgery attack, attackers can extract the watermark by inverting the watermarked image and re-generating it with an arbitrary prompt, thereby enabling false attribution on malicious content. In this paper, we propose the CSGuard, the first forgery-resistant watermarking schema that leverages compressed sensing to bind the watermarked image generation and verification to a secret matrix. This ensures that only users possessing the secret matrix can correctly embed or verify the image watermark, prevents the illegal users from forgery without compromising generation quality and watermark integrity. Experimental results demonstrate that CSGuard achieves strong forgery resistance, reduces the attack success rate from 100.0\% to 28.12\%, and achieve 100\% detection rate on benign watermarked images without compromising watermarking effectiveness.

preprint2022arXiv

Reinforcement Learning Based Robust Policy Design for Relay and Power Optimization in DF Relaying Networks

In this paper, we study the outage minimization problem in a decode-and-forward cooperative network with relay uncertainty. To reduce the outage probability and improve the quality of service, existing researches usually rely on the assumption of both exact instantaneous channel state information (CSI) and environmental uncertainty. However, it is difficult to obtain perfect instantaneous CSI immediately under practical situations where channel states change rapidly, and the uncertainty in communication environments may not be observed, which makes traditional methods not applicable. Therefore, we turn to reinforcement learning (RL) methods for solutions, which do not need any prior knowledge of underlying channel or assumptions of environmental uncertainty. RL method is to learn from the interaction with communication environment, optimize its action policy, and then propose relay selection and power allocation schemes. We first analyse the robustness of RL action policy by giving the lower bound of the worst-case performance, when RL methods are applied to communication scenarios with environment uncertainty. Then, we propose a robust algorithm for outage probability minimization based on RL. Simulation results reveal that compared with traditional RL methods, our approach has better generalization ability and can improve the worst-case performance by about 6% when evaluated in unseen environments.