StableGuard: Towards Unified Copyright Protection and Tamper Localization in Latent Diffusion Models

Yi Wang (Beijing University of Posts and Telecommunications) · Shengfeng He (Singapore Management University) · Yuyang Yu (South China University of Technology) · Xuemiao Xu (South China University of Technology) · Haoxin Yang (South China University of Technology) · Bangzhen Liu (City University of Hong Kong) · Cheng Xu (Hong Kong Polytechnic University) · Zikai Huang (South China University of Technology)

binary watermarkcopyright protectiondiffusion modelsforensic reliabilitylatent diffusion modelsmixture-of-experts guided forensic networkmultiplexing watermark vaepost hoc processingself-supervised optimizationstableguardtampered region detectiontampering localizationtampering-agnostic forensic networkvariational autoencoderwatermark verification

The advancement of diffusion models has enhanced the realism of AI-generated content but also raised concerns about misuse, necessitating robust copyright protection and tampering localization. Although recent methods have made progress toward unified solutions, their reliance on post hoc processing introduces considerable application inconvenience and compromises forensic reliability. We propose StableGuard, a novel framework that seamlessly integrates a binary watermark into the diffusion generation process, ensuring copyright protection and tampering localization in Latent Diffusion Models through an end-to-end design. We develop a Multiplexing Watermark VAE (MPW-VAE) by equipping a pretrained Variational Autoencoder (VAE) with a lightweight latent residual-based adapter, enabling the generation of paired watermarked and watermark-free images. These pairs, fused via random masks, create a diverse dataset for training a tampering-agnostic forensic network. To further enhance forensic synergy, we introduce a Mixture-of-Experts Guided Forensic Network (MoE-GFN) that dynamically integrates holistic watermark patterns, local tampering traces, and frequency-domain cues for precise watermark verification and tampered region detection. The MPW-VAE and MoE-GFN are jointly optimized in a self-supervised, end-to-end manner, fostering a reciprocal training between watermark embedding and forensic accuracy. Extensive experiments demonstrate that StableGuard consistently outperforms state-of-the-art methods in image fidelity, watermark verification, and tampering localization.