Security evaluation of biometric authentication systems under real spoofing attacks

By Battista Biggio, Zahid Akhtar, Giorgio Fumera, Gian Luca Marcialis, Fabio Roli. 2012

160 citations recorded by the source corpus.

Subfields: Genetics, Information Systems, Signal Processing

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Citations in the corpus

Security Evaluation of Pattern Classifiers under Attack (2014, background): “the fake traits; however, this is a challenging research issue on its own, that is out of the scope of this work, and part of the authors’ ongoing work [47], [48].”
Adversarial Feature Selection Against Evasion Attacks (2016, background): “, impostors and genuine users in biometric recognition systems; spam and ham emails in spam filtering) [1]–[7].”
A Survey on Anti-Spooﬁng Schemes for Fingerprint Recognition Systems (2014, methodology): “The investigation about how well real spoof attacks can be simulated using match scores has been carried out on different datasets by [Biggio et al. 2012] and [Akhtar et al. 2011b].”
Identifying Security Evaluation of PatternClassifiers Under attack (2015, background): “Well known examples of attacks against pattern classifiers are: submitting a fake biometric trait to a biometric authentication system (spoofing attack) [1], [2]; Well known examples of attacks are: Spoofing attacks where one person or program purposely falsifying data and thereby gaining an illegit”
Automatic adaptation of fingerprint liveness detector to new spoof materials (2014, background): “The success rate of such spoof attacks can be up to 70% [12, 2].”
Evaluation of serial and parallel multibiometric systems under spoofing attacks (2012, methodology): “However, some recent works [13, 12, 10, 3, 5, 7] have questioned the validity of this belief, and have empirically shown that spoofing only one trait is sufficient to evade a parallel multi-modal system.”
Understanding OSN-based facial disclosure against face authentication systems (2014, background): “Therefore, it is generally considered sufficiently secure as an authentication factor for common access protection [5], as we observe that many face authentication systems [10, 8, 38, 24, 25, 39] such as Google’s Face Unlock and Lenovo’s Veriface, are widely available on all kinds of consumer-level ”
Pattern Recognition Systems under Attack: Design Issues and Research Challenges (2014, background): “The following examples will help understanding how our model can be exploited to identify and analyze non-trivial attacks.”
Spoofing Face Recognition With 3D Masks (2014, background): “However, this advantage becomes a weakness in malicious circumstances, enabling attackers to create copies and spoof face recognition systems without any difficulties.”
Pattern Recognition Systems under Attack (2013, background): “The fake (or spoofed) faces can be obtained by printing a face image on paper [3].”
Adversarial Biometric Recognition : A review on biometric system security from the adversarial machine-learning perspective (2015, methodology): “This procedure is illustrated in Figure 1, which is general enough to also account for multibiometric systems; in this case, the fusion [FIg1] the architecture”
Multi-Factor Authentication: A Survey (2018, background): “All of those are typically vulnerable to a variety of attacks at entirely different levels, ranging from replay attempts to adversary attacks [175].”
Poisoning Adaptive Biometric Systems (2012, methodology): “The data set is available under request to the authors, and it was also used in [2].”