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A Recipe for Watermarking Diffusion Models (2303.10137v2)

Published 17 Mar 2023 in cs.CV, cs.CR, and cs.LG

Abstract: Diffusion models (DMs) have demonstrated advantageous potential on generative tasks. Widespread interest exists in incorporating DMs into downstream applications, such as producing or editing photorealistic images. However, practical deployment and unprecedented power of DMs raise legal issues, including copyright protection and monitoring of generated content. In this regard, watermarking has been a proven solution for copyright protection and content monitoring, but it is underexplored in the DMs literature. Specifically, DMs generate samples from longer tracks and may have newly designed multimodal structures, necessitating the modification of conventional watermarking pipelines. To this end, we conduct comprehensive analyses and derive a recipe for efficiently watermarking state-of-the-art DMs (e.g., Stable Diffusion), via training from scratch or finetuning. Our recipe is straightforward but involves empirically ablated implementation details, providing a foundation for future research on watermarking DMs. The code is available at https://github.com/yunqing-me/WatermarkDM.

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Authors (6)
  1. Yunqing Zhao (9 papers)
  2. Tianyu Pang (96 papers)
  3. Chao Du (83 papers)
  4. Xiao Yang (159 papers)
  5. Ngai-Man Cheung (80 papers)
  6. Min Lin (97 papers)
Citations (86)

Summary

  • The paper introduces a learned encoder-decoder framework to embed binary watermarks, achieving reliable detection even under image alterations.
  • It quantifies the trade-off between watermark complexity and image quality using FID, suggesting higher resolution images can mitigate quality loss.
  • For text-to-image models, a trigger prompt with weight-constrained finetuning ensures robust watermark embedding while preserving synthesis performance.

A Recipe for Watermarking Diffusion Models: An Analysis

The paper, "A Recipe for Watermarking Diffusion Models," provides a comprehensive analysis of methodologies to embed watermarks into diffusion models (DMs), which have shown remarkable performance in generative tasks, particularly in image synthesis. As these models become more integrated into practical applications, issues such as copyright protection and the detection of generated content have emerged. This work aims to address these legal and monitoring challenges by proposing frameworks for watermarking both unconditional/class-conditional and text-to-image diffusion models.

Unconditional and Class-Conditional Generation

The paper begins by tackling the watermarking of DMs designed for unconditional and class-conditional generation. The authors propose embedding a binary watermark string into the training data itself. This is accomplished through a learned encoder-decoder framework that integrates a binary string into the data, which remains detectable via a pretrained decoder. The paper evaluates this approach across datasets like CIFAR-10, FFHQ, AFHQv2, and ImageNet-1K. Results show that predefined watermarks are successfully decoded from generated images, even after subjected to various attacks such as Gaussian noise, brightening, and random masking, indicating robustness.

However, a notable downside is the degradation of generative quality with increased watermark complexity. This trade-off is quantified using metrics like FID, where higher bit-lengths of watermarks result in lower image quality. The paper highlights that increasing image resolution could mitigate quality loss, pointing toward potential improvements in future model architectures or training processes to better incorporate watermarks without impacting performance adversely.

Text-to-Image Generation

For text-to-image DMs, such as Stable Diffusion, the paper proposes a more sophisticated methodology by finetuning existing models instead of training from scratch. By using a trigger prompt paired with a watermark image, the model can produce the embedded watermark upon receiving the specific text input. This process is executed through a weight-constrained finetuning objective, which incorporates regularization to limit performance degradation on non-trigger prompts.

The empirical results validate that this regularization method maintains high-quality image synthesis capabilities while successfully embedding the watermark. This method effectively balances watermark robustness against potential performance trade-offs, offering a scalable and interchangeable solution for watermarks as it allows rapid updates across model changes.

Implications and Future Directions

The implications of embedding watermarks into DMs resonate across both legal and operational domains. By enabling inherent model-level copyright protection and content origin tracking, watermarks could facilitate adherence to intellectual property laws and counteract misuse of generative models. This potentially plays a crucial role in the responsible deployment of these models in commercial and creative applications. However, a balance must be struck between embedding robustness and generative quality, especially as the complexity of watermarks increases.

Furthermore, while the current approach demonstrates efficacy, the paper recognizes the need for future research to diminish quality degradation. Enhanced encoder-decoder architectures, improved training paradigms, or more effective integration methods for higher watermark bit-lengths without performance loss are potential avenues for exploration.

In conclusion, this paper contributes significantly to the dialogue surrounding the ethical and legal deployment of powerful DMs. The authors' systematic investigation into watermarking practices sets a precedent for the continued development of responsible AI technologies, advocating for solutions that align robust protection with minimal compromise on performance. Future innovations will likely expand upon these ideas, fostering advancements toward safer and more controlled generative model applications.

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