Watermarking is a tool for actively identifying and attributing the images
generated by latent diffusion models. Existing methods face the dilemma of
watermark robustness and image quality. The reason for this dilemma is that
watermark detection is performed in pixel space, implying an intrinsic link
between image quality and watermark robustness. In this paper, we highlight
that an effective solution to the problem is to both inject and detect
watermarks in latent space, and propose Latent Watermark (LW) with a
progressive training strategy. Experiments show that compared to the recently
proposed methods such as StegaStamp, StableSignature, RoSteALS and TreeRing, LW
not only surpasses them in terms of robustness but also offers superior image
quality. When we inject 64-bit messages, LW can achieve an identification
performance close to 100% and an attribution performance above 97% under 9
single-attack scenarios and one all-attack scenario. Our code will be available
on GitHub.

本文提出了一种在潜在空间中注入和检测水印的解决方案 —— 潜在水印（LW），并采用渐进训练策略。通过实验证明，在注入 64 位消息时，LW 在 9 个单次攻击场景和一个全攻击场景下，可以实现接近 100% 的识别性能和 97% 以上的归属性能。

潜水印：在潜在扩散空间中注入和检测水印

Latent Watermark: Inject and Detect Watermarks in Latent Diffusion Space

This paper investigates the weaknesses of image watermarking techniques. We
present WAVES (Watermark Analysis Via Enhanced Stress-testing), a novel
benchmark for assessing watermark robustness, overcoming the limitations of
current evaluation methods.WAVES integrates detection and identification tasks,
and establishes a standardized evaluation protocol comprised of a diverse range
of stress tests. The attacks in WAVES range from traditional image distortions
to advanced and novel variations of adversarial, diffusive, and embedding-based
attacks. We introduce a normalized score of attack potency which incorporates
several widely used image quality metrics and allows us to produce of an
ordered ranking of attacks. Our comprehensive evaluation over reveals
previously undetected vulnerabilities of several modern watermarking
algorithms. WAVES is envisioned as a toolkit for the future development of
robust watermarking systems.

该研究探讨了图像水印技术的弱点，提出了一种用于评估水印鲁棒性的新型基准测试工具 WAVES（通过增强的压力测试进行水印分析），克服了当前评估方法的局限性。WAVES 整合了检测和识别任务，建立了一个由各种压力测试构成的标准评估协议。WAVES 中的攻击范围从传统的图像失真到先进和新颖的对抗性、扩散性和嵌入性攻击。我们引入了一个攻击效能的归一化得分，该得分结合了几种广泛使用的图像质量指标，并允许我们生成一个攻击排序。我们的综合评估揭示了几种现代水印算法先前未发现的漏洞。WAVES 被设想为未来开发鲁棒水印系统的工具包。