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This paper presents a five-stage pipeline designed to enhance pedestrian privacy in intelligent transportation systems by employing face-swapping techniques while preserving essential facial attributes. The study evaluates two face-swapping models, Roop and Ghost-v2, demonstrating that Roop significantly outperforms Ghost-v2 in maintaining image usability without compromising privacy. This approach addresses the critical need for diverse datasets in autonomous vehicle training while mitigating security risks associated with identity theft and tracking.
Roop's face-swapping model achieves a breakthrough in balancing pedestrian privacy and data usability, outperforming existing methods.
Large-scale and diverse datasets are needed to train AI models to take real-time decisions for autonomous vehicles (AVs), an intelligent transportation system (ITS) application. Pedestrian intention and trajectory prediction are critical models used in AVs, requiring datasets involving diverse pedestrian images. Unrestricted access to these datasets imposes serious security risks, like identity theft and pedestrian tracking. The challenge is to apply privacy preservation procedures while maintaining the image attributes needed to train the models. Existing privacy methods may preserve the pedestrian's privacy, but degrade the image usability, which hinders the models'effectiveness. This work's focus is to implement a five-stage pipeline to protect pedestrians'privacy through face swapping while keeping the essential facial attributes intact. It should be tailored to satisfy the privacy needs of the Egy-DRiVeS dataset. Moreover, Roop and Ghost-v2 face-swapping models are evaluated. Provenly, Roop outperforms Ghost-v2 in various aspects, as will be discussed. Consequently, Roop is the face-swapping model to be used in the pipeline to strike the balance between pedestrian privacy via identity concealment and data usability via facial attribute preservation.
