An evaluation of indirect attacks and countermeasures in fingerprint verification systems

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Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning (2017, background): “…here, we refer the reader to few practical examples of such attacks reported to date, including model inversion attacks aimed to steal machine learning models and earlier hill-climbing attacks against biometric systems used to steal the face and ﬁngerprint templates of their users [28, 62, 67–70].”
Pattern Recognition Systems under Attack: Design Issues and Research Challenges (2014, background): “However, the presence of intelligent, adaptive adversaries who exploit their knowledge of the targeted system to evolve their attack strategy over time, introduces a further source of adversarial non-stationarity, that can be modeled by introducing a random variable A, as p(X,Y,A; t)[41, 20] (see…”
Security evaluation of biometric authentication systems under real spoofing attacks (2012, background): “Besides the so-called “indirect attacks”, which require some knowledge of the system or access to its internal parameters [32, 1, 13, 24], much more attention has been devoted to the so-called “direct attacks”, namely, attacks which consist of submitting a counterfeited biometric (i.e., a replica…”