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Hypersphere Secure Sketch Revisited: Probabilistic Linear Regression Attack on IronMask in Multiple Usage

arXiv 2409.12884 ↗First posted 19 Sep 2024cs.CR cs.CVcited by 0

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Abstract (arXiv)

Protection of biometric templates is a critical and urgent area of focus. IronMask demonstrates outstanding recognition performance while protecting facial templates against existing known attacks. In high-level, IronMask can be conceptualized as a fuzzy commitment scheme building on the hypersphere directly. We devise an attack on IronMask targeting on the security notion of renewability. Our attack, termed as Probabilistic Linear Regression Attack, utilizes the linearity of underlying used error correcting code. This attack is the first algorithm to successfully recover the original template when getting multiple protected templates in acceptable time and requirement of storage. We implement experiments on IronMask applied to protect ArcFace that well verify the validity of our attacks. Furthermore, we carry out experiments in noisy environments and confirm that our attacks are still applicable. Finally, we put forward two strategies to mitigate this type of attacks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 54 references that are in the map. First posted on arXiv 19 Sep 2024.

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References · 10

  1. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,833
  2. 2017SphereFace: Deep Hypersphere Embedding for Face Recognitioncited by 733
  3. 2021MagFace: A Universal Representation for Face Recognition and Quality Assessmentcited by 332
  4. 2017On the Reconstruction of Face Images from Deep Face Templatescited by 66
  5. 2017Ranking Based Locality Sensitive Hashing Enabled Cancelable Biometrics: Index-of-Max Hashingcited by 21
  6. 2019Zero-Shot Deep Hashing and Neural Network Based Error Correction for Face Template Protectioncited by 9
  7. 2021IronMask: Modular Architecture for Protecting Deep Face Templatecited by 9
  8. 2019Inference in High-Dimensional Linear Regression via Lattice Basis Reduction and Integer Relation Detectioncited by 5
  9. 2020Neural Fuzzy Extractors: A Secure Way to Use Artificial Neural Networks for Biometric User Authenticationcited by 2
  10. 2021Revisiting Fuzzy Signatures: Towards a More Risk-Free Cryptographic Authentication System based on Biometricscited by 2

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