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

By Battista Biggio, Giorgio Fumera, Paolo Russu, Luca Didaci, Fabio Roli. 2015

98 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Genetics, Safety, Robustness & Interpretability, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Battista Biggio
2. Fabio Roli
3. S. Hailes
4. Nilufer Tuptuk
5. Giorgio Fumera
6. Giorgio Giacinto
7. Davide Maiorca
8. Ambra Demontis

In Genetics

1. Fabio Roli
2. Battista Biggio
3. Giorgio Fumera

In Safety, Robustness & Interpretability

1. Battista Biggio
2. Fabio Roli
3. Giorgio Fumera
4. Giorgio Giacinto
5. Davide Maiorca
6. Ambra Demontis
7. Nicolas Papernot
8. Nicholas Carlini

In Signal Processing

1. Battista Biggio
2. Fabio Roli
3. Giorgio Fumera
4. Giorgio Giacinto
5. Davide Maiorca
6. Ambra Demontis
7. Nicholas Carlini
8. Marco Melis

Citations in the corpus

• Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning (2017, methodology): “…(poisoning attacks) [22–31] and at test time (evasion attacks) [18– 20, 28, 32–37]; ( ii ) propose systematic methodologies for security evaluation of learning algorithms against such attacks [37– 41]; and ( iii ) design suitable defense mechanisms to mitigate these threats [11, 18, 33, 42–46].”
• Security of smart manufacturing systems (2018, background): “Examples of attack on machine learning include spam filtering [35], malware classifiers [36–38] and biometric recognition systems [39].”