Universal Adversarial Spoofing Attacks against Face Recognition

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 method, 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.

[Link][BibTeX]

 @INPROCEEDINGS{9484380,
  author={Amada, Takuma and Liew, Seng Pei and Kakizaki, Kazuya and Araki, Toshinori},
  booktitle={2021 IEEE International Joint Conference on Biometrics (IJCB)}, 
  title={Universal Adversarial Spoofing Attacks against Face Recognition}, 
  year={2021},
  volume={},
  number={},
  pages={1-7},
  doi={10.1109/IJCB52358.2021.9484380}}