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With rapid advancements in image generation technology, face swapping forprivacy protection has emerged as an active area of research. The ultimatebenefit is improved access to video datasets, e.g. in healthcare settings.Recent literature has proposed deep network-based architectures to performfacial swaps and reported the associated reduction in facial recognitionaccuracy. However, there is not much reporting on how well these methodspreserve the types of semantic information needed for the privatized videos toremain useful for their intended application. Our main contribution is a novelend-to-end face swapping pipeline for recorded videos of standardizedassessments of autism symptoms in children. Through this design, we are thefirst to provide a methodology for assessing the privacy-utility trade-offs forthe face swapping approach to patient privacy protection. Our methodology canshow, for example, that current deep network based face swapping isbottle-necked by face detection in real world videos, and the extent to whichgaze and expression information is preserved by face swaps relative to baselineprivatization methods such as blurring.

Practical Digital Disguises: Leveraging Face Swaps to Protect Patient Privacy