Secure Computation of Face Identification (SCiFI) [20] is a recently developed secure face recognition system that ensures the list of faces it can identify (e.g., a terrorist watch list) remains private. In this work, we study the consequences of malformed input attacks on the system - from both a security and computer vision standpoint. In particular, we present 1) a cryptographic attack that allows a dishonest user to undetectably obtain a coded representation of faces on the list, and 2) a visualization approach that exploits this breach, turning the lossy recovered codes into human-identifiable face sketches. We evaluate our approach on two challenging datasets, with face identification tasks given to a computer and human subjects. Whereas prior work considered security in the setting of honest inputs and protocol execution, the success of our approach underscores the risk posed by malicious adversaries to todays automatic face recognition systems.
['Andy Luong', 'Michael Gerbush', 'Brent Waters', 'Kristen Grauman']
Reconstructing a fragmented face from a cryptographic identification protocol