系统梳理AI生成图像水印技术，助力内容溯源与可信生态建设
Secure and Robust Watermarking for AI-generated Images: A Comprehensive Survey
- 从机制设计到安全评估，全面解析水印技术五大核心维度
- 总结现有方法在视觉保真度、嵌入容量与可检测性上的权衡关系
- 聚焦抗攻击能力与未来挑战，适合关注AI内容安全的研究者参考
生成式人工智能的快速发展使高质量视觉内容的合成变得极为便捷，但也引发了知识产权保护、真实性验证和责任追溯等紧迫问题。水印技术作为追踪内容来源、区分AI生成图像与自然图像、支撑可信数字生态的基础手段，日益受到重视。本文对AI生成图像水印技术进行系统性综述，从五个方面展开：(1) 水印系统的形式化定义与基础组件；(2) 现有水印方法及其特性对比；(3) 视觉保真度、嵌入容量与可检测性等评价指标；(4) 面向恶意攻击的已知漏洞及近期安全鲁棒水印设计进展；(5) 当前开放挑战、新兴趋势与未来研究方向。本综述旨在为研究人员提供对AI生成图像水印技术的全面理解，推动其向更安全、更负责任的AI内容实践发展。
原文摘要 · Abstract (English)
The rapid progress of Generative Artificial Intelligence (GenAI) has enabled the effortless synthesis of high-quality visual content, while simultaneously raising pressing concerns about intellectual property protection, authenticity, and accountability. Among various countermeasures, watermarking has emerged as a fundamental mechanism for tracing provenance, distinguishing AI-generated images from natural content, and supporting trustworthy digital ecosystems. This paper presents a comprehensive survey of AI-generated image watermarking, systematically reviewing the field from five perspectives: (1) the formalization and fundamental components of image watermarking systems; (2) existing watermarking methodologies and their comparative characteristics; (3) evaluation metrics in terms of visual fidelity, embedding capacity, and detectability; (4) known vulnerabilities under malicious attacks and recent advances in secure and robust watermarking designs; and (5) open challenges, emerging trends, and future research directions. The survey seeks to offer researchers a holistic understanding of watermarking technologies for AI-generated images and to facilitate their continued advancement toward secure and responsible AI-generated content practices.
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