[2603.27513] Understanding Semantic Perturbations on In-Processing Generative Image Watermarks

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.27513 (cs) [Submitted on 29 Mar 2026] Title:Understanding Semantic Perturbations on In-Processing Generative Image Watermarks Authors:Anirudh Nakra, Min Wu View a PDF of the paper titled Understanding Semantic Perturbations on In-Processing Generative Image Watermarks, by Anirudh Nakra and 1 other authors View PDF HTML (experimental) Abstract:The widespread deployment of high-fidelity generative models has intensified the need for reliable mechanisms for provenance and content authentication. In-processing watermarking, embedding a signature into the generative model's synthesis procedure, has been advocated as a solution and is often reported to be robust to standard post-processing (such as geometric transforms and filtering). Yet robustness to semantic manipulations that alter high-level scene content while maintaining reasonable visual quality is not well studied or understood. We introduce a simple, multi-stage framework for systematically stress-testing in-processing generative watermarks under semantic drift. The framework utilizes off-the-shelf models for object detection, mask generation, and semantically guided inpainting or regeneration to produce controlled, meaning-altering edits with minimal perceptual degradation. Based on extensive experiments on representative schemes, we find that robustness varies significantly with the degree of semantic entanglement: methods by which watermarks remai...