Paper
10 September 2024 Research on intellectual property protection algorithm of deep semantic segmentation model
Author Affiliations +
Proceedings Volume 13257, International Conference on Advanced Image Processing Technology (AIPT 2024); 1325713 (2024) https://doi.org/10.1117/12.3040856
Event: International Conference on Advanced Image Processing Technology (AIPT 2024), 2024, Chongqing, China
Abstract
In its judicial interpretation, the Supreme People's Court clarified that algorithmic information can be protected by trade secrets by way of enumeration, which reflects the increased importance of algorithmic trade secret protection. Deep models have high intellectual property value and commercial value, and have even become the core competitiveness of some individuals or small businesses, making them the target of attacks by some malicious competitors and lawless elements. Current digital watermarking technology has been extended to deep neural networks, but these methods are only suitable for protecting the intellectual property of classification models. Therefore, this article proposes research on the intellectual property protection of semantic segmentation models. This paper abandons the traditional method of selecting trigger sets in classification model protection, and proposes an adversarial generation method to independently design trigger sets, embed patterns or symbols with special markings into pictures, and then use a backdoor mechanism to embed trigger set digital watermarks into semantic segmentation In the model, the backdoor disadvantages of the model are converted into backdoor advantages, making the segmentation model more discriminative during the verification process.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Na Chen, Ying Li, and Gang Zheng "Research on intellectual property protection algorithm of deep semantic segmentation model", Proc. SPIE 13257, International Conference on Advanced Image Processing Technology (AIPT 2024), 1325713 (10 September 2024); https://doi.org/10.1117/12.3040856
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KEYWORDS
Digital watermarking

Image segmentation

Data modeling

Semantics

Intellectual property

Performance modeling

Education and training

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