mlsanityData to 7 Oct 2026

Is Face Recognition Safe from Realizable Attacks?

arXiv 2210.08178 ↗First posted 15 Oct 2022cs.CV cs.LGcited by 1

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Abstract (arXiv)

Face recognition is a popular form of biometric authentication and due to its widespread use, attacks have become more common as well. Recent studies show that Face Recognition Systems are vulnerable to attacks and can lead to erroneous identification of faces. Interestingly, most of these attacks are white-box, or they are manipulating facial images in ways that are not physically realizable. In this paper, we propose an attack scheme where the attacker can generate realistic synthesized face images with subtle perturbations and physically realize that onto his face to attack black-box face recognition systems. Comprehensive experiments and analyses show that subtle perturbations realized on attackers face can create successful attacks on state-of-the-art face recognition systems in black-box settings. Our study exposes the underlying vulnerability posed by the Face Recognition Systems against realizable black-box attacks.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 30 references that are in the map. First posted on arXiv 15 Oct 2022.

Builds on

15 of 30 references.

References · 15

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  4. 2016Adversarial examples in the physical worldcited by 1,762
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  6. 2016Practical Black-Box Attacks against Machine Learningcited by 893
  7. 2017ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Modelscited by 656
  8. 2017Adversarial Patchcited by 514
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  10. 2017Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Modelscited by 459
  11. 2019Efficient Decision-based Black-box Adversarial Attacks on Face Recognitioncited by 173
  12. 2017Synthesizing Robust Adversarial Examplescited by 39
3 more
  1. 2018Unravelling Robustness of Deep Learning based Face Recognition Against Adversarial Attackscited by 31
  2. 2017Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Imagescited by 20
  3. 2018Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifierscited by 10

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Citing papers per year: 2023: 1.

1 paper.

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