Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data

Abstract

Guaranteeing the security of transactional systems is a crucial priority of all institutions that process transactions, in order to protect their businesses against cyberattacks and fraudulent attempts. Adversarial attacks are novel techniques that, other than being proven to be effective to fool image classification models, can also be applied to tabular data. Adversarial attacks aim at producing adversarial examples, in other words, slightly modified inputs that induce the Artificial Intelligence (AI) system to return incorrect outputs that are advantageous for the attacker. In this paper we illustrate a novel approach to modify and adapt state-of-the-art algorithms to imbalanced tabular data, in the context of fraud detection. Experimental results show that the proposed modifications lead to a perfect attack success rate, obtaining adversarial examples that are also less perceptible when analyzed by humans. Moreover, when applied to a real-world production system, the proposed techniques shows the possibility of posing a serious threat to the robustness of advanced AI-based fraud detection procedures.

Will be published on Proceedings of the Workshop on Artificial Intelligence Safety (SafeAI 2021) co-located with 35th AAAI Conference on Artificial Intelligence (AAAI 2021)

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Cited by in corpus (5)

• When and How to Fool Explainable Models (and Humans) with Adversarial Examples
• The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples
• Investigating Imperceptibility of Adversarial Attacks on Tabular Data: An Empirical Analysis
• Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis
• Meta-Learning for Unsupervised Outlier Detection with Optimal Transport