Adversarial Biometric Recognition : A review on biometric system security from the adversarial machine-learning perspective

@article{Biggio2015AdversarialBR,
  title={Adversarial Biometric Recognition : A review on biometric system security from the adversarial machine-learning perspective},
  author={Battista Biggio and Giorgio Fumera and Paolo Russu and Luca Didaci and Fabio Roli},
  journal={IEEE Signal Processing Magazine},
  year={2015},
  volume={32},
  pages={31-41}
}
In this article, we review previous work on biometric security under a recent framework proposed in the field of adversarial machine learning. This allows us to highlight novel insights on the security of biometric systems when operating in the presence of intelligent and adaptive attackers that manipulate data to compromise normal system operation. We show how this framework enables the categorization of known and novel vulnerabilities of biometric recognition systems, along with the… 
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References

SHOWING 1-10 OF 34 REFERENCES
Poisoning attacks to compromise face templates
TLDR
This paper shows that the attack on PCA-based face verification templates can be successful even in the case of multiple templates per client, for different matchers, and under more realistic scenarios, and validate it by experiments to highlight its practical relevance.
Security evaluation of biometric authentication systems under real spoofing attacks
TLDR
The results confirm that multimodal systems are vulnerable to attacks against a single biometric trait, and show that the `worst-case` scenario can be too pessimistic, which can lead to two conservative choices.
Biometric Template Security
TLDR
This work presents a high-level categorization of the various vulnerabilities of a biometric system and discusses countermeasures that have been proposed to address these vulnerabilities.
Poisoning Adaptive Biometric Systems
TLDR
It is shown how a carefully designed attack may gradually poison the template gallery of some users, and successfully mislead a simple PCA-based face verification system that performs self-update.
Security Evaluation of Pattern Classifiers under Attack
TLDR
A framework for empirical evaluation of classifier security that formalizes and generalizes the main ideas proposed in the literature, and given examples of its use in three real applications show that security evaluation can provide a more complete understanding of the classifier's behavior in adversarial environments, and lead to better design choices.
Vulnerabilities in Biometric Encryption Systems
  • A. Adler
  • Computer Science, Mathematics
    AVBPA
  • 2005
TLDR
A potential vulnerability in biometric encryption systems that allows a less-than-brute force regeneration of the secret and an estimate of the enrolled image is described.
Multimodal fusion vulnerability to non-zero effort (spoof) imposters
TLDR
A method for assessment of multimodal systems and underlying fusion algorithms is outlined and experiments are conducted on a multi-modal database of face, iris, and fingerprint match scores.
On the vulnerability of face verification systems to hill-climbing attacks
Robustness of multimodal biometric fusion methods against spoof attacks
An evaluation of indirect attacks and countermeasures in fingerprint verification systems
...
1
2
3
4
...