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arXiv · 2110.00708

Universal Adversarial Spoofing Attacks against Face Recognition

Takuma Amada·Seng Pei Liew·Kazuya Kakizaki·Toshinori Araki
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Abstract

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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BibTeXRIS

Takuma Amada, Seng Pei Liew, Kazuya Kakizaki, Toshinori Araki. 2021-10-02. Universal Adversarial Spoofing Attacks against Face Recognition. https://doi.org/10.1109/ijcb52358.2021.9484380

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