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Secure and Robust Watermarking for AI-generated Images: A Comprehensive Survey

Jie Cao, Qi Li, Zelin Zhang, Jianbing Ni

TL;DR

The paper surveys watermarking for AI-generated images, formalizing a rigorous framework (WM) with explicit notions of robustness and security. It distinguishes traditional domain-based and deep-learning-based methods, then focuses on in-generation watermarking via fine-tuning and initial-noise strategies within diffusion/LDM frameworks, including TreeRing, RingID, Gaussian Shading, PRC, and related approaches. It provides a comprehensive evaluation landscape across visual quality, capacity, detectability, and benchmarks like WAVES and MarkDiffusion, and analyzes attacks ranging from common perturbations to removal and forgery, proposing defense mechanisms such as data augmentation and redundancy coding. The work culminates with open questions on structured, publicly verifiable, and multi-stakeholder watermarking, underscoring the practical significance of robust, secure provenance and IP protection in real-world Gen-AI ecosystems.

Abstract

The rapid advancement of generative artificial intelligence (Gen-AI) has facilitated the effortless creation of high-quality images, while simultaneously raising critical concerns regarding intellectual property protection, authenticity, and accountability. Watermarking has emerged as a promising solution to these challenges by distinguishing AI-generated images from natural content, ensuring provenance, and fostering trustworthy digital ecosystems. This paper presents a comprehensive survey of the current state of AI-generated image watermarking, addressing five key dimensions: (1) formalization of image watermarking systems; (2) an overview and comparison of diverse watermarking techniques; (3) evaluation methodologies with respect to visual quality, capacity, and detectability; (4) vulnerabilities to malicious attacks; and (5) prevailing challenges and future directions. The survey aims to equip researchers with a holistic understanding of AI-generated image watermarking technologies, thereby promoting their continued development.

Secure and Robust Watermarking for AI-generated Images: A Comprehensive Survey

TL;DR

The paper surveys watermarking for AI-generated images, formalizing a rigorous framework (WM) with explicit notions of robustness and security. It distinguishes traditional domain-based and deep-learning-based methods, then focuses on in-generation watermarking via fine-tuning and initial-noise strategies within diffusion/LDM frameworks, including TreeRing, RingID, Gaussian Shading, PRC, and related approaches. It provides a comprehensive evaluation landscape across visual quality, capacity, detectability, and benchmarks like WAVES and MarkDiffusion, and analyzes attacks ranging from common perturbations to removal and forgery, proposing defense mechanisms such as data augmentation and redundancy coding. The work culminates with open questions on structured, publicly verifiable, and multi-stakeholder watermarking, underscoring the practical significance of robust, secure provenance and IP protection in real-world Gen-AI ecosystems.

Abstract

The rapid advancement of generative artificial intelligence (Gen-AI) has facilitated the effortless creation of high-quality images, while simultaneously raising critical concerns regarding intellectual property protection, authenticity, and accountability. Watermarking has emerged as a promising solution to these challenges by distinguishing AI-generated images from natural content, ensuring provenance, and fostering trustworthy digital ecosystems. This paper presents a comprehensive survey of the current state of AI-generated image watermarking, addressing five key dimensions: (1) formalization of image watermarking systems; (2) an overview and comparison of diverse watermarking techniques; (3) evaluation methodologies with respect to visual quality, capacity, and detectability; (4) vulnerabilities to malicious attacks; and (5) prevailing challenges and future directions. The survey aims to equip researchers with a holistic understanding of AI-generated image watermarking technologies, thereby promoting their continued development.

Paper Structure

This paper contains 49 sections, 35 equations, 8 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Watermarking and Its Role in Trustworthy AI
  3. Security and Robustness of AI-Generated Image Watermarking
  4. Fundamentals of Image Watermarking
  5. Notations
  6. Terminology
  7. Common Definition of Image Watermark
  8. The embedding operator Embed_\\ Lambda
  9. Feasible-set projection (generalizing clip).
  10. Example: post-hoc transform-domain embedding with projection.
  11. Optimization variants.
  12. Other domains
  13. Instantiation of the watermark by embedding family.
  14. Robustness Definition
  15. Security Definition
  16. ...and 34 more sections

Figures (8)

  • Figure 1: Number of publications in the field of AI-generated image and its watermarking (the data is sourced from Semantic Scholar).
  • Figure 2: Watermarking scenario overview for Gen-AI. This figure illustrates the core components of watermarking for AI-generated images, covering watermark embedding, verification, and potential attacks.
  • Figure 3: Traditional invisible image watermarking pipeline.
  • Figure 4: DL-based image watermark system architecture.
  • Figure 5: Taxonomy of AI-generated image watermarking methods.
  • ...and 3 more figures

Theorems & Definitions (4)

  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4