With rapid advancements in image generation technology, face swapping for
privacy protection has emerged as an active area of research. The ultimate
benefit is improved access to video datasets, e.g. in healthcare settings.
Recent literature has proposed deep network-based architectures to perform
facial swaps and reported the associated reduction in facial recognition
accuracy. However, there is not much reporting on how well these methods
preserve the types of semantic information needed for the privatized videos to
remain useful for their intended application. Our main contribution is a novel
end-to-end face swapping pipeline for recorded videos of standardized
assessments of autism symptoms in children. Through this design, we are the
first to provide a methodology for assessing the privacy-utility trade-offs for
the face swapping approach to patient privacy protection. Our methodology can
show, for example, that current deep network based face swapping is
bottle-necked by face detection in real world videos, and the extent to which
gaze and expression information is preserved by face swaps relative to baseline
privatization methods such as blurring.

本文提出了一种新颖的端到端人脸交换流水线用于评估面孔交换方法在隐私保护方面的效益和使用效用之间的平衡以及与基线隐私方法的差异，研究表明当前基于深度网络人脸交换受到现实世界视频中面部检测的瓶颈限制，文章针对标准儿童自闭症症状评估记录视频进行研究。