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Visual Watermarking in the Era of Diffusion Models: Advances and Challenges

Junxian Duan, Jiyang Guan, Wenkui Yang, Ran He

TL;DR

This survey examines how diffusion models enable robust, imperceptible visual watermarking to protect ownership and traceability of generative content. It categorizes watermarking approaches into data-driven passive, sampling-driven passive, and adversarial proactive methods, and analyzes their effectiveness and robustness under attacks, including denoising. The work discusses computational trade-offs, multi-attribution capabilities, and practical applications in copyright protection, forensics, and privacy, while highlighting emerging trends such as combining diffusion with vision-language models and federated learning. Overall, diffusion watermarking offers a promising path for safeguarding intellectual property and privacy in a rapidly evolving generative AI landscape, but significant challenges in efficiency, robustness, and governance remain.

Abstract

As generative artificial intelligence technologies like Stable Diffusion advance, visual content becomes more vulnerable to misuse, raising concerns about copyright infringement. Visual watermarks serve as effective protection mechanisms, asserting ownership and deterring unauthorized use. Traditional deepfake detection methods often rely on passive techniques that struggle with sophisticated manipulations. In contrast, diffusion models enhance detection accuracy by allowing for the effective learning of features, enabling the embedding of imperceptible and robust watermarks. We analyze the strengths and challenges of watermark techniques related to diffusion models, focusing on their robustness and application in watermark generation. By exploring the integration of advanced diffusion models and watermarking security, we aim to advance the discourse on preserving watermark robustness against evolving forgery threats. It emphasizes the critical importance of developing innovative solutions to protect digital content and ensure the preservation of ownership rights in the era of generative AI.

Visual Watermarking in the Era of Diffusion Models: Advances and Challenges

TL;DR

This survey examines how diffusion models enable robust, imperceptible visual watermarking to protect ownership and traceability of generative content. It categorizes watermarking approaches into data-driven passive, sampling-driven passive, and adversarial proactive methods, and analyzes their effectiveness and robustness under attacks, including denoising. The work discusses computational trade-offs, multi-attribution capabilities, and practical applications in copyright protection, forensics, and privacy, while highlighting emerging trends such as combining diffusion with vision-language models and federated learning. Overall, diffusion watermarking offers a promising path for safeguarding intellectual property and privacy in a rapidly evolving generative AI landscape, but significant challenges in efficiency, robustness, and governance remain.

Abstract

As generative artificial intelligence technologies like Stable Diffusion advance, visual content becomes more vulnerable to misuse, raising concerns about copyright infringement. Visual watermarks serve as effective protection mechanisms, asserting ownership and deterring unauthorized use. Traditional deepfake detection methods often rely on passive techniques that struggle with sophisticated manipulations. In contrast, diffusion models enhance detection accuracy by allowing for the effective learning of features, enabling the embedding of imperceptible and robust watermarks. We analyze the strengths and challenges of watermark techniques related to diffusion models, focusing on their robustness and application in watermark generation. By exploring the integration of advanced diffusion models and watermarking security, we aim to advance the discourse on preserving watermark robustness against evolving forgery threats. It emphasizes the critical importance of developing innovative solutions to protect digital content and ensure the preservation of ownership rights in the era of generative AI.
Paper Structure (20 sections, 6 equations, 1 figure, 1 table)

This paper contains 20 sections, 6 equations, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Preliminary
  3. Visual Watermarking Overview
  4. Diffusion Models: A New Paradigm
  5. Diffusion Models for Visual Watermarking
  6. Watermarking in Diffusion Models
  7. Passive Watermarking.
  8. Proactive Watermarking.
  9. Effectiveness of Watermarking
  10. Robustness of Watermarking
  11. Challenges and Open Issues
  12. Computational Efficiency of Model Watermarking
  13. Attack against Watermarks
  14. Multi-Attribution Watermarks
  15. Applications of Visual Watermarking with Diffusion Models
  16. ...and 5 more sections

Figures (1)

  • Figure 1: Three common workflows for DMs watermarking. (Top) Data-driven passive methods require specific training data to be pre-embedded with watermarks, allowing the watermark to be transferred from data into the DMs. (Middle) Sampling-driven passive methods modify DMs' sampling strategies, altering the output distribution to embed watermarks. (Bottom) Adversarial proactive methods apply adversarial attacks against DMs to introduce adversarial watermarks, leading to unrecognizable examples that hinder effective customized outputs.