A Black-Box Watermarking Modulation for Object Detection Models - Confiance.ai
Conference Papers Year : 2024

A Black-Box Watermarking Modulation for Object Detection Models

Mohammed Lansari
Lucas Mattioli
  • Function : Author
Boussad Addad
  • Function : Author
Paul-Marie Raffi
  • Function : Author
Martin Gonzalez
  • Function : Author
Mohamed Ibn Khedher
  • Function : Author

Abstract

Training a Deep Neural Network (DNN) from scratch comes with a substantial cost in terms of money, energy, data, and hardware. When such models are misused or redistributed without authorisation, the owner faces significant financial and intellectual property (IP) losses. Therefore, there is a pressing need to protect the IP of Machine Learning models to avoid these issues. ML watermarking emerges as a promising solution for model traceability. Watermarking has been well-studied for image classification models, but there is a significant research gap in its application to other tasks like object detection, for which no effective methods have been proposed yet. In this paper, we introduce a novel black-box watermarking method for object detection models. Our contributions include a watermarking technique that maps visual information to text semantics and a comparative study of fine-tuning techniques' impact on watermark detectability. We present the model's detection performance and evaluate fine-tuning strategies' effectiveness in preserving watermark integrity.
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Dates and versions

hal-04726701 , version 1 (08-10-2024)

Identifiers

  • HAL Id : hal-04726701 , version 1

Cite

Mohammed Lansari, Lucas Mattioli, Boussad Addad, Paul-Marie Raffi, Katarzyna Kapusta, et al.. A Black-Box Watermarking Modulation for Object Detection Models. AI Trustworthiness and Risk Assessment for Challenged Contexts workshop (ATRACC). AAAI 2024 Fall Symposium, Nov 2024, Arlington, United States. ⟨hal-04726701⟩
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