Untargeted Near-collision Attacks on Biometrics: Real-world Bounds and
Theoretical Limits
- URL: http://arxiv.org/abs/2304.01580v5
- Date: Wed, 21 Feb 2024 10:18:57 GMT
- Title: Untargeted Near-collision Attacks on Biometrics: Real-world Bounds and
Theoretical Limits
- Authors: Axel Durbet and Paul-Marie Grollemund and Kevin Thiry-Atighehchi
- Abstract summary: 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A biometric recognition system can operate in two distinct modes:
identification or verification. In the first mode, the system recognizes an
individual by searching the enrolled templates of all the users for a match. In
the second mode, the system validates a user's identity claim by comparing the
fresh provided template with the enrolled template. The biometric
transformation schemes usually produce binary templates that are better handled
by cryptographic schemes, and the comparison is based on a distance that leaks
information about the similarities between two biometric templates. Both the
experimentally determined false match rate and false non-match rate through
recognition threshold adjustment define the recognition accuracy, and hence the
security of the system. To our knowledge, few works provide a formal treatment
of security in case of minimal information leakage, i.e., the binary outcome of
a comparison with a threshold. In this paper, we focus on untargeted attacks
that can be carried out both online and offline, and in both identification and
verification modes. On the first hand, we focus our analysis on the accuracy
metrics of biometric systems. We provide the complexity of untargeted attacks
using the False Match Rate (FMR) and the False Positive Identification Rate
(FPIR) to address the security of these systems. Studying near-collisions with
these metrics allows us to estimate the maximum number of users in a database,
given a chosen FMR, to preserve the security and the accuracy. These results
are evaluated on systems from the literature. On the other hand, we rely on
probabilistic modelling to assess the theoretical security limits of biometric
systems. The study of this metric space, and system parameters (template size,
threshold and database size), gives us the complexity of untargeted attacks and
the probability of a near-collision.
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