We assess the vulnerabilities of deep face recognition systems for images
that falsify/spoof multiple identities simultaneously. We demonstrate that, by
manipulating the deep feature representation extracted from a face image via
imperceptibly small perturbations added at the pixel level using our proposed
Universal Adversarial Spoofing Examples (UAXs), one can fool a face
verification system into recognizing that the face image belongs to multiple
different identities with a high success rate. One characteristic of the UAXs
crafted with our method is that they are universal (identity-agnostic); they
are successful even against identities not known in advance. For a certain deep
neural network, we show that we are able to spoof almost all tested identities
(99%), including those not known beforehand (not included in training). Our
results indicate that a multiple-identity attack is a real threat and should be
taken into account when deploying face recognition systems.

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