Guidance Watermarking for Diffusion Models

2025-09-26

AI Summary

Addressing the trade-off between robustness and generation quality in diffusion model watermarking, this paper proposes a plug-and-play watermarking method that requires no fine-tuning of either the watermark decoder or the generative model. Methodologically, it leverages gradient signals from an off-the-shelf watermark decoder to introduce a differentiable watermark guidance mechanism during diffusion sampling, augmented by a multi-scale enhancement strategy to improve resilience against common attacks—including cropping and compression. The approach seamlessly migrates arbitrary post-hoc watermarking schemes into the generative process, ensuring compatibility with mainstream diffusion samplers (e.g., SD, DDIM) and diverse watermark detectors, without compromising image fidelity or sample diversity. Experiments demonstrate that watermark embedding negligibly perturbs the original generation distribution, achieves significantly higher robustness than baseline methods, and supports synergistic integration with VAE-layer watermarking for further enhancement.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

This paper introduces a novel watermarking method for diffusion models. It is based on guiding the diffusion process using the gradient computed from any off-the-shelf watermark decoder. The gradient computation encompasses different image augmentations, increasing robustness to attacks against which the decoder was not originally robust, without retraining or fine-tuning. Our method effectively convert any extit{post-hoc} watermarking scheme into an in-generation embedding along the diffusion process. We show that this approach is complementary to watermarking techniques modifying the variational autoencoder at the end of the diffusion process. We validate the methods on different diffusion models and detectors. The watermarking guidance does not significantly alter the generated image for a given seed and prompt, preserving both the diversity and quality of generation.

Problem

• Develops watermarking guidance for diffusion models during generation
• Enhances robustness against attacks without retraining decoders
• Converts post-hoc schemes into in-generation embedding process

Innovation

• Guides diffusion process using watermark decoder gradient
• Converts post-hoc watermarking into in-generation embedding
• Enhances robustness without retraining through image augmentations