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

2025-09-22

AI Summary

To address the challenge of simultaneously ensuring copyright protection and precise tampering localization for diffusion model-generated content, this paper proposes StableGuard—the first end-to-end jointly optimized framework that embeds watermarks and detects tampering concurrently in the latent space. Methodologically, it introduces a Multi-Purpose Watermarking VAE (MPW-VAE) to generate paired watermarked/unwatermarked images, augmented with randomized masks for diverse training data; and a Mixture-of-Experts Guided Forensic Network (MoE-GFN) that jointly models global watermark signals, local tampering artifacts, and frequency-domain features—enabling real-time copyright verification and pixel-level tampering localization without post-processing. Experiments demonstrate that StableGuard significantly outperforms state-of-the-art methods in image fidelity, watermark detection rate (>99.2%), and tampering localization mIoU (+8.7%), while maintaining high robustness and practical applicability.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Security and Privacy: Data transparency and provenance

Abstract

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.

Problem

• Protecting copyright and detecting tampering in AI-generated images
• Overcoming limitations of post-processing methods for content protection
• Integrating watermarking directly into latent diffusion model generation

Innovation

• Integrates binary watermark directly into diffusion generation process
• Uses Multiplexing Watermark VAE with latent residual adapter
• Employs Mixture-of-Experts network for forensic analysis