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

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.