Latent watermarking methods have limited robustness to image changes
On the Intrinsic Limited Robustness of Latent-Based Watermarking
Machine LearningCryptography and Security
Summary
Watermarking is a way to secretly mark images to prove ownership. The authors found that common watermarking methods that hide marks in hidden layers of AI image models are not very strong against changes like rotating, scaling, or moving the image. They explain why these methods struggle with such changes using math, and they confirm their explanation through experiments. This means current watermarking methods may fail when images are mildly edited.
What this means in practice
- •For digital content protectors: Assess the vulnerability of latent watermarking in AI-generated images to common image edits like rotation and scaling.
- •For image editing software developers: Design watermark detection tools that account for theoretical robustness limits against geometric image modifications.
Authors
Cheng-Han Yeh, Kuan-chun Yu, Cheng-Chang Tsai, Chun-Shien Lu
Abstract
Existing latent-based watermarking methods for diffusion models have overestimated their robustness to image distortions, including geometric transformations such as rotation, scaling, and translation (RST). Moreover, this paradigm of watermarking approaches may suffer from inherent limitations arising from the domain in which the watermark is embedded. In this paper, we provide the first theoretical analysis explaining why these methods lack invariance to perturbations. By relaxing the invariant relation, we derive a maximum perturbation bound that characterizes the relationship between pixel-space perturbations and their corresponding effects in latent space. In addition, we present the first analytical formulation that captures all components of practical detection mechanisms. Finally, we conduct experiments to validate the theoretical findings and the limitations of latent-based watermarking methods. Our theoretical and empirical results indicate that, under the current design paradigm, latent-based watermarking methods intrinsically exhibit limited robustness. We conclude by providing the analytical tool and design guidelines that future research could follow.