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Content scanning systems employ perceptual hashing algorithms to scan usercontent for illegal material, such as child pornography or terroristrecruitment flyers. Perceptual hashing algorithms help determine whether twoimages are visually similar while preserving the privacy of the input images.Several efforts from industry and academia propose to conduct content scanningon client devices such as smartphones due to the impending roll out ofend-to-end encryption that will make server-side content scanning difficult.However, these proposals have met with strong criticism because of thepotential for the technology to be misused and re-purposed. Our work informsthis conversation by experimentally characterizing the potential for one typeof misuse -- attackers manipulating the content scanning system to performphysical surveillance on target locations. Our contributions are threefold: (1)we offer a definition of physical surveillance in the context of client-sideimage scanning systems; (2) we experimentally characterize this risk and createa surveillance algorithm that achieves physical surveillance rates of >40% bypoisoning 5% of the perceptual hash database; (3) we experimentally study thetrade-off between the robustness of client-side image scanning systems andsurveillance, showing that more robust detection of illegal material leads toincreased potential for physical surveillance.

Re-purposing Perceptual Hashing based Client Side Scanning for Physical Surveillance