Authentication Attacks on Projection-based Cancelable Biometric Schemes
- URL: http://arxiv.org/abs/2110.15163v1
- Date: Thu, 28 Oct 2021 14:39:35 GMT
- Title: Authentication Attacks on Projection-based Cancelable Biometric Schemes
- Authors: Axel Durbet, Pascal Lafourcade, Denis Migdal, Kevin Thiry-Atighehchi
and Paul-Marie Grollemund
- Abstract summary: 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)
- Score: 0.6499759302108924
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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. This type of transformation is constructed as a
composition of a biometric transformation with a feature extraction algorithm.
The security requirements of cancelable biometric schemes concern the
irreversibility, unlinkability and revocability of templates, without losing in
accuracy of comparison. While several schemes were recently attacked regarding
these requirements, full reversibility of such a composition in order to
produce colliding biometric characteristics, and specifically presentation
attacks, were never demonstrated to the best of our knowledge. 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). Solving these optimization problems allows an adversary to
slightly alter its fingerprint image in order to impersonate any individual.
Moreover, in an even more severe scenario, it is possible to simultaneously
impersonate several individuals.
Related papers
- Enhancing Privacy in Face Analytics Using Fully Homomorphic Encryption [8.742970921484371]
We propose a novel technique that combines Fully Homomorphic Encryption (FHE) with an existing template protection scheme known as PolyProtect.
Our proposed approach ensures irreversibility and unlinkability, effectively preventing the leakage of soft biometric embeddings.
arXiv Detail & Related papers (2024-04-24T23:56:03Z) - Privacy-preserving Multi-biometric Indexing based on Frequent Binary
Patterns [7.092869001331781]
We propose an efficient privacy-preserving multi-biometric identification system that retrieves protected deep cancelable templates.
A multi-biometric binning scheme is designed to exploit the low intra-class variation properties contained in the frequent binary patterns extracted from different types of biometric characteristics.
arXiv Detail & Related papers (2023-10-04T18:18:24Z) - 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) - Versatile Weight Attack via Flipping Limited Bits [68.45224286690932]
We study a novel attack paradigm, which modifies model parameters in the deployment stage.
Considering the effectiveness and stealthiness goals, we provide a general formulation to perform the bit-flip based weight attack.
We present two cases of the general formulation with different malicious purposes, i.e., single sample attack (SSA) and triggered samples attack (TSA)
arXiv Detail & Related papers (2022-07-25T03:24:58Z) - Quantum Proofs of Deletion for Learning with Errors [91.3755431537592]
We construct the first fully homomorphic encryption scheme with certified deletion.
Our main technical ingredient is an interactive protocol by which a quantum prover can convince a classical verifier that a sample from the Learning with Errors distribution in the form of a quantum state was deleted.
arXiv Detail & Related papers (2022-03-03T10:07:32Z) - Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth
Uncertainty Learning [54.15303628138665]
Face anti-spoofing (FAS) plays a vital role in preventing face recognition systems from presentation attacks.
Existing face anti-spoofing datasets lack diversity due to the insufficient identity and insignificant variance.
We propose Dual Spoof Disentanglement Generation framework to tackle this challenge by "anti-spoofing via generation"
arXiv Detail & Related papers (2021-12-01T15:36:59Z) - 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) - Random Hash Code Generation for Cancelable Fingerprint Templates using
Vector Permutation and Shift-order Process [3.172761915061083]
We propose a non-invertible distance preserving scheme based on vector permutation and shift-order process.
A shift-order process is then applied to the generated features in order to achieve non-invertibility and combat similarity-based attacks.
The generated hash codes are resilient to different security and privacy attacks whilst fulfilling the major revocability and unlinkability requirements.
arXiv Detail & Related papers (2021-05-21T09:37:54Z) - Targeted Attack against Deep Neural Networks via Flipping Limited Weight
Bits [55.740716446995805]
We study a novel attack paradigm, which modifies model parameters in the deployment stage for malicious purposes.
Our goal is to misclassify a specific sample into a target class without any sample modification.
By utilizing the latest technique in integer programming, we equivalently reformulate this BIP problem as a continuous optimization problem.
arXiv Detail & Related papers (2021-02-21T03:13:27Z) - Certified Robustness to Label-Flipping Attacks via Randomized Smoothing [105.91827623768724]
Machine learning algorithms are susceptible to data poisoning attacks.
We present a unifying view of randomized smoothing over arbitrary functions.
We propose a new strategy for building classifiers that are pointwise-certifiably robust to general data poisoning attacks.
arXiv Detail & Related papers (2020-02-07T21:28:30Z)
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.