A Black-Box Watermarking Modulation for Semantic Segmentation Models

Mohammed Lansari, Lucas Mattioli, Boussad ADDAD, Paul-Marie RAFFI, Martin Gonzalez, Katarzyna Kapusta

Keywords: ML Watermarking, ML Traceability, Digital Right Management, Semantic Segmentation

Abstract:

The capability of clearly identifying the origin of a ML model is an
important element of trustworthy AI. First standardisation reports
highlight the necessity of providing ML traceability, while pointing
out that existing tools for Digital Right Management are not sufficient
in the context of ML. Watermarking has been explored as a possible
answer for this need, and has been implemented for image classification
models, but there remains a substantial research gap in its application
to other tasks such as object detection or semantic segmentation,
which remains largely unexplored. In this paper, we propose a
novel black-box watermarking technique specifically designed for
semantic segmentation. Our contributions include a novel watermarking method links visual data
to text semantics and provide comparative analysis of the effect of
fine-tuning techniques on watermark detectability. Finally, we highlight several
regulatory recommendations on how to design watermarking techniques for
segmentation purposes.

Submission Number: 61