Deep CNN Face Matchers Inherently Support Revocable Biometric Templates

• URL: http://arxiv.org/abs/2506.18731v1
• Date: Mon, 23 Jun 2025 15:09:04 GMT
• Title: Deep CNN Face Matchers Inherently Support Revocable Biometric Templates
• Authors: Aman Bhatta, Michael C. King, Kevin W. Bowyer,
• Abstract summary: A biometric scheme is revocable if an individual can have their current enrollment in the scheme revoked.<n>We show that modern deep CNN face matchers inherently allow for a robust revocable biometric scheme.
• Score: 7.448523610526267
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One common critique of biometric authentication is that if an individual's biometric is compromised, then the individual has no recourse. The concept of revocable biometrics was developed to address this concern. A biometric scheme is revocable if an individual can have their current enrollment in the scheme revoked, so that the compromised biometric template becomes worthless, and the individual can re-enroll with a new template that has similar recognition power. We show that modern deep CNN face matchers inherently allow for a robust revocable biometric scheme. For a given state-of-the-art deep CNN backbone and training set, it is possible to generate an unlimited number of distinct face matcher models that have both (1) equivalent recognition power, and (2) strongly incompatible biometric templates. The equivalent recognition power extends to the point of generating impostor and genuine distributions that have the same shape and placement on the similarity dimension, meaning that the models can share a similarity threshold for a 1-in-10,000 false match rate. The biometric templates from different model instances are so strongly incompatible that the cross-instance similarity score for images of the same person is typically lower than the same-instance similarity score for images of different persons. That is, a stolen biometric template that is revoked is of less value in attempting to match the re-enrolled identity than the average impostor template. We also explore the feasibility of using a Vision Transformer (ViT) backbone-based face matcher in the revocable biometric system proposed in this work and demonstrate that it is less suitable compared to typical ResNet-based deep CNN backbones.

Related papers

• Cancelable Biometric Template Generation Using Random Feature Vector Transformations [3.536605202672355]
  Cancelable biometric schemes are designed to extract an identity-preserving, non-invertible as well as revocable pseudo-identifier from biometric data.<n>State-of-the-art cancelable schemes generate pseudo-identifiers by transforming the original template using either user-specific salting or many-to-one transformations.<n>A novel, modality-independent cancelable biometric scheme is proposed to overcome these limitations.
  arXiv  Detail & Related papers  (2025-03-19T19:03:04Z)
• Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception? [86.58989831070426]
  We study the faithfulness of hand-crafted metrics to human perception of privacy information from reconstructed images. We propose a learning-based measure called SemSim to evaluate the Semantic Similarity between the original and reconstructed images.
  arXiv  Detail & Related papers  (2023-09-22T17:58:04Z)
• Voice Morphing: Two Identities in One Voice [12.404748962951157]
  We introduce Voice Identity Morphing (VIM) - a voice-based morph attack that can synthesize speech samples that impersonate the voice characteristics of a pair of individuals. VIM has a success rate (MMPMR) of over 80% at a false match rate of 1% on the Librispeech dataset.
  arXiv  Detail & Related papers  (2023-09-05T17:36:34Z)
• An Explainable Model-Agnostic Algorithm for CNN-based Biometrics Verification [55.28171619580959]
  This paper describes an adaptation of the Local Interpretable Model-Agnostic Explanations (LIME) AI method to operate under a biometric verification setting.
  arXiv  Detail & Related papers  (2023-07-25T11:51:14Z)
• Untargeted Near-collision Attacks on Biometrics: Real-world Bounds and Theoretical Limits [0.0]
  We focus on untargeted attacks that can be carried out both online and offline, and in both identification and verification modes. We use the False Match Rate (FMR) and the False Positive Identification Rate (FPIR) to address the security of these systems. The study of this metric space, and system parameters, gives us the complexity of untargeted attacks and the probability of a near-collision.
  arXiv  Detail & Related papers  (2023-04-04T07:17:31Z)
• OTB-morph: One-Time Biometrics via Morphing [16.23764869038004]
  This paper introduces a new idea to exploit as a transformation function for cancelable biometrics. An experimental implementation of the proposed scheme is given for face biometrics.
  arXiv  Detail & Related papers  (2023-02-17T18:39:40Z)
• OTB-morph: One-Time Biometrics via Morphing applied to Face Templates [8.623680649444212]
  This paper introduces a new scheme for cancelable biometrics aimed at protecting the templates against potential attacks. An experimental implementation of the proposed scheme is given for face biometrics.
  arXiv  Detail & Related papers  (2021-11-25T18:35:34Z)
• Benchmarking Quality-Dependent and Cost-Sensitive Score-Level Multimodal Biometric Fusion Algorithms [58.156733807470395]
  This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign. The campaign targeted the application of physical access control in a medium-size establishment with some 500 persons. To the best of our knowledge, this is the first attempt to benchmark quality-based multimodal fusion algorithms.
  arXiv  Detail & Related papers  (2021-11-17T13:39:48Z)
• Authentication Attacks on Projection-based Cancelable Biometric Schemes [0.6499759302108924]
  Cancelable biometric schemes aim at generating secure biometric templates by combining user specific tokens, such as password, stored secret or salt, along with biometric data. The security requirements of cancelable biometric schemes concern the irreversibility, unlinkability and revocability of templates, without losing in accuracy of comparison. In this paper, we formalize these attacks for a traditional cancelable scheme with the help of integer linear programming (ILP) and quadratically constrained quadratic programming (QCQP)
  arXiv  Detail & Related papers  (2021-10-28T14:39:35Z)
• Reversible Watermarking in Deep Convolutional Neural Networks for Integrity Authentication [78.165255859254]
  We propose a reversible watermarking algorithm for integrity authentication. The influence of embedding reversible watermarking on the classification performance is less than 0.5%. At the same time, the integrity of the model can be verified by applying the reversible watermarking.
  arXiv  Detail & Related papers  (2021-04-09T09:32:21Z)
• AttriMeter: An Attribute-guided Metric Interpreter for Person Re-Identification [100.3112429685558]
  Person ReID systems only provide a distance or similarity when matching two persons. We propose an Attribute-guided Metric Interpreter, named AttriMeter, to semantically and quantitatively explain the results of CNN-based ReID models.
  arXiv  Detail & Related papers  (2021-03-02T03:37:48Z)
• Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification [48.4619620405991]
  Mammogram benign or malignant classification with only image-level labels is challenging due to the absence of lesion annotations. Motivated by the symmetric prior, we can explore a counterfactual problem that how would the features have behaved if there were no lesions in the image. We derive a new theoretical result for counterfactual generation based on the symmetric prior.
  arXiv  Detail & Related papers  (2020-09-30T03:15:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.