Understanding Semantic Perturbations on In-Processing Generative Image Watermarks

2026-03-29

AI Summary

Existing image watermarking methods exhibit insufficient robustness under semantic-level edits and lack systematic evaluation. This work proposes a multi-stage stress-testing framework that leverages off-the-shelf models for object detection, semantic segmentation, and image inpainting or generation to construct controllable semantic editing pipelines. For the first time, it systematically reveals the detrimental impact of semantic manipulations on embedded watermarks. Experimental results demonstrate that even when edited images retain high visual fidelity, the detection rates of mainstream watermarking schemes plummet to near zero, exposing a critical gap in current evaluation protocols: the neglect of semantic robustness.

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 remain detectable under a broad suite of conventional perturbations can fail under semantic edits, with watermark detectability in many cases dropping to near zero while image quality remains high. Overall, our results reveal a critical gap in current watermarking evaluations and suggest that watermark designs and benchmarking must explicitly account for robustness against semantic manipulation.

Problem

• semantic perturbations
• generative image watermarking
• in-processing watermarking
• content authentication
• semantic manipulation

Innovation

• semantic perturbations
• in-processing watermarking
• generative image watermark
• semantic robustness
• content authentication