通过换脸保护行人隐私，同时保留关键面部特征用于自动驾驶训练。
Swapping Faces, Saving Features: A Dual-Purpose Pipeline for Pedestrian Privacy in ITS
- 设计五阶段流程，用换脸技术隐藏身份但保留可用面部特征。
- 在Egy-DRiVeS数据集上验证，Roop模型比Ghost-v2更优。
- 适合需要隐私保护的智能交通系统数据处理场景。
大规模且多样化的数据集对于训练自动驾驶车辆（AV）所需的实时决策AI模型至关重要，其中行人意图与轨迹预测是关键模型，依赖于包含多样化行人图像的数据集。然而，对这些数据集的无限制访问会带来严重安全风险，如身份盗用和行人追踪。核心挑战在于，在保护隐私的同时维持图像对模型训练的有效性。现有隐私保护方法虽能隐藏身份，但会降低图像可用性，影响模型性能。本文提出一种五阶段管道，通过人脸替换实现隐私保护，同时保持必要的面部属性。该方案专为Egy-DRiVeS数据集定制。对比评估表明，Roop模型在多个方面优于Ghost-v2，因此被选为管道中的核心换脸模型，可在身份隐匿与数据可用性之间取得良好平衡。
原文摘要 · Abstract (English)
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.
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