Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers

Sotgiu A.;Demontis A.;Biggio B.;Roli F.
In press

Abstract

Adversarial attacks on machine learning-based classifiers, along with defense mechanisms, have been widely studied in the context of single-label classification problems. In this paper, we shift the attention to multi-label classification, where the availability of domain knowledge on the relationships among the considered classes may offer a natural way to spot incoherent predictions, i.e., predictions associated to adversarial examples lying outside of the training data distribution. We explore this intuition in a framework in which first-order logic knowledge is converted into constraints and injected into a semi-supervised learning problem. Within this setting, the constrained classifier learns to fulfill the domain knowledge over the marginal distribution, and can naturally reject samples with incoherent predictions. Even though our method does not exploit any knowledge of attacks during training, our experimental analysis surprisingly unveils that domain-knowledge constraints can help detect adversarial examples effectively, especially if such constraints are not known to the attacker. We show how to implement an adaptive attack exploiting knowledge of the constraints and, in a specifically-designed setting, we provide experimental comparisons with popular state-of-the-art attacks. We believe that our approach may provide a significant step towards designing more robust multi-label classifiers.
2021
Inglese
PP
1
1
1
Esperti anonimi
internazionale
scientifica
Adversarial machine learning
adversarial machine learning
domain knowledge
Learning from constraints
multi-label classification
Robustness
Semi-supervised learning
Task analysis
Training
Training data
no
Melacci, S.; Ciravegna, G.; Sotgiu, A.; Demontis, A.; Biggio, B.; Gori, M.; Roli, F.
1.1 Articolo in rivista
info:eu-repo/semantics/article
1 Contributo su Rivista::1.1 Articolo in rivista
262
7
none
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