Who Wore It Best? And Who Paid Less? Effects of Privacy-Preserving Techniques Across Demographics

Xavier Merino, Michael King; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2024, pp. 1160-1169

Face recognition technologies, widely adopted across various domains, have raised concerns related to demographic differentials in performance and the erosion of personal privacy. This study explores the potential of "cloaking"--a privacy-preserving technique subtly altering facial images at the pixel level in order to reduce recognition accuracy--in addressing these concerns. Specifically, we assess the effectiveness of the state-of-the-art Fawkes algorithm across demographic groups categorized by race (i.e., African American and Caucasian) and gender. Our findings reveal African American males as the most significant beneficiaries of this protective measure. Moreover, in terms of cost-effectiveness, the African American demographic, as a collective, enjoys greater protection with fewer visual disruptions compared to Caucasians. Nevertheless, we caution that while cloaking techniques like Fawkes bolster individual privacy, their protection may not remain absolute as recognition algorithms advance. Thus, we underscore the persistent need for prudent online data-sharing practices.