Swapping Faces, Saving Features: A Dual-Purpose Pipeline for Pedestrian Privacy in ITS

2026-07-09

AI Summary

This study addresses the privacy risks associated with identity leakage in pedestrian images used for training autonomous driving models within intelligent transportation systems, where existing anonymization methods often degrade facial attributes critical for model performance. To reconcile this trade-off, the authors propose a five-stage, dual-objective processing pipeline that achieves a balance between identity anonymization and preservation of essential facial features. The work presents the first systematic evaluation of Roop and Ghost-v2 for this specific task on the Egy-DRiVeS dataset. Experimental results demonstrate that Roop outperforms Ghost-v2 across multiple metrics, effectively safeguarding privacy while maintaining data utility. Consequently, the anonymized images remain suitable for training models tasked with pedestrian intention and trajectory prediction.

Abstract

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.

Problem

• pedestrian privacy
• face swapping
• data usability
• facial attributes
• intelligent transportation system

Innovation

• face swapping
• privacy preservation
• pedestrian attribute retention
• intelligent transportation systems
• autonomous vehicles