Enhancing privacy protection in intelligent and connected vehicles: a styleGAN3-based image desensitization dataset and framework

Jingyan Wang; Liyong Wang; Haojie Ji; Junzhe Fang; Teng Guo

doi:10.1117/12.3049582

8 November 2024 Enhancing privacy protection in intelligent and connected vehicles: a styleGAN3-based image desensitization dataset and framework

Jingyan Wang, Liyong Wang, Haojie Ji, Junzhe Fang, Teng Guo

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 1341629 (2024) https://doi.org/10.1117/12.3049582
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

Intelligent and connected vehicles (ICVs) collect vast video footage, inadvertently capturing privacy-sensitive data. To address this, we introduce the IDD-ICV dataset, featuring diverse in-vehicle camera footage. To protect privacy, we propose a styleGAN3 based framework that integrates MHA (Multi-Head Attention) for detecting and desensitizing facial data in datasets. This approach ensures compliance with data security standards and enhances ICVs' privacy protection capabilities during the life cycle of data. Our study contributes to the development of secure and trustworthy autonomous transportation systems by demonstrating the effectiveness of our framework in safeguarding privacy-sensitive information.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jingyan Wang, Liyong Wang, Haojie Ji, Junzhe Fang, and Teng Guo "Enhancing privacy protection in intelligent and connected vehicles: a styleGAN3-based image desensitization dataset and framework", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 1341629 (8 November 2024); https://doi.org/10.1117/12.3049582

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