Gaussian Shading++: Rethinking the Realistic Deployment Challenge of Performance-Lossless Image Watermark for Diffusion Models

2025-04-21

AI Summary

Existing diffusion model watermarking methods overlook practical deployment bottlenecks: cumbersome key management, variable user-defined generation parameters, and infeasible third-party verification. This paper proposes a robust, real-world-oriented watermarking framework. Methodologically, it (1) introduces a novel dual-channel architecture that decouples watermark embedding from verification; (2) models the generation-inversion process as an additive white Gaussian noise (AWGN) channel and designs a soft-decision decoder, significantly enhancing robustness across diverse sampling steps, schedulers, and inversion methods; and (3) integrates public-key digital signatures to enable open, forgery-resistant third-party verification without requiring access to the generative model. Experiments demonstrate that the method achieves zero degradation in image generation quality while fully supporting user-customizable parameters and trusted external verification—thereby overcoming critical barriers to real-world deployment.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Generation

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Data transparency and provenance

Abstract

Ethical concerns surrounding copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models. One effective solution involves watermarking the generated images. Existing methods primarily focus on ensuring that watermark embedding does not degrade the model performance. However, they often overlook critical challenges in real-world deployment scenarios, such as the complexity of watermark key management, user-defined generation parameters, and the difficulty of verification by arbitrary third parties. To address this issue, we propose Gaussian Shading++, a diffusion model watermarking method tailored for real-world deployment. We propose a double-channel design that leverages pseudorandom error-correcting codes to encode the random seed required for watermark pseudorandomization, achieving performance-lossless watermarking under a fixed watermark key and overcoming key management challenges. Additionally, we model the distortions introduced during generation and inversion as an additive white Gaussian noise channel and employ a novel soft decision decoding strategy during extraction, ensuring strong robustness even when generation parameters vary. To enable third-party verification, we incorporate public key signatures, which provide a certain level of resistance against forgery attacks even when model inversion capabilities are fully disclosed. Extensive experiments demonstrate that Gaussian Shading++ not only maintains performance losslessness but also outperforms existing methods in terms of robustness, making it a more practical solution for real-world deployment.

Problem

• Addresses copyright and content concerns in diffusion models
• Solves key management and third-party verification challenges
• Ensures robust watermarking with varying generation parameters

Innovation

• Double-channel design with pseudorandom error-correcting codes
• Soft decision decoding for robustness against distortions
• Public key signatures for third-party verification