mlsanityData to 14 Oct 2026

AdvBiom: Adversarial Attacks on Biometric Matchers

arXiv 2301.03966 ↗First posted 10 Jan 2023cs.CVcited by 0

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Abstract (arXiv)

With the advent of deep learning models, face recognition systems have achieved impressive recognition rates. The workhorses behind this success are Convolutional Neural Networks (CNNs) and the availability of large training datasets. However, we show that small human-imperceptible changes to face samples can evade most prevailing face recognition systems. Even more alarming is the fact that the same generator can be extended to other traits in the future. In this work, we present how such a generator can be trained and also extended to other biometric modalities, such as fingerprint recognition systems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 40 of its 63 references that are in the map. First posted on arXiv 10 Jan 2023.

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40 of 63 references.

References · 40

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  3. 2022Generative Adversarial Networkscited by 5,627
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  12. 2016Universal adversarial perturbationscited by 784
28 more
  1. 2017SphereFace: Deep Hypersphere Embedding for Face Recognitioncited by 737
  2. 2018Improving Transferability of Adversarial Examples with Input Diversitycited by 599
  3. 2016Delving into Transferable Adversarial Examples and Black-box Attackscited by 536
  4. 2018Black-box Adversarial Attacks with Limited Queries and Informationcited by 501
  5. 2017Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Modelscited by 462
  6. 2014Learning Face Representation from Scratchcited by 453
  7. 2019Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attackscited by 442
  8. 2015Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networkscited by 214
  9. 2018Spatially Transformed Adversarial Examplescited by 195
  10. 2019Efficient Decision-based Black-box Adversarial Attacks on Face Recognitioncited by 175
  11. 2019AdvHat: Real-world adversarial attack on ArcFace Face ID systemcited by 163
  12. 2019Improving Black-box Adversarial Attacks with a Transfer-based Priorcited by 121
  13. 2021Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognitioncited by 97
  14. 2019NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networkscited by 94
  15. 2019SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editingcited by 93
  16. 2017A General Framework for Adversarial Examples with Objectivescited by 84
  17. 2020Towards Transferable Adversarial Attack against Deep Face Recognitioncited by 80
  18. 2019AdvFaces: Adversarial Face Synthesiscited by 60
  19. 2019Learning a Fixed-Length Fingerprint Representationcited by 49
  20. 2022Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognitioncited by 43
  21. 2020Adversarial Light Projection Attacks on Face Recognition Systems: A Feasibility Studycited by 40
  22. 2018Attacks on State-of-the-Art Face Recognition using Attentional Adversarial Attack Generative Networkcited by 38
  23. 2017Synthesizing Normalized Faces from Facial Identity Featurescited by 33
  24. 2018Fast Geometrically-Perturbed Adversarial Facescited by 22
  25. 2018Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimizationcited by 18
  26. 2022RSTAM: An Effective Black-Box Impersonation Attack on Face Recognition using a Mobile and Compact Printercited by 7
  27. 2021Improving the Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimatorcited by 6
  28. 2019White-Box Evaluation of Fingerprint Matchers: Robustness to Minutiae Perturbationscited by 2

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