Work

XSub: Explanation-Driven Adversarial Attack against Blackbox Classifiers via Feature Substitution

Year: 2024

Type: conference-paper

Abstract: Despite its significant benefits in enhancing the transparency and trustworthiness of artificial intelligence (AI) systems, explainable AI (XAI) can unintentionally provide adversaries with insights into blackbox models, increasing their vulnerability to various attacks. In this paper, we develop a novel explanation-driven adversarial attack against blackbox classifiers based on feature substitution, called XSub. The key idea of XSub is to strategically replace important features (identified via XAI) in the original sample with corresponding important features of a different label, thereby increasing the likelihood of the model misclassifying the perturbed sample. XSub only requires a minimal number of queries and can be easily extended to launch backdoor attacks in case the attacker has access to the model’s training data. Our evaluation shows that XSub is not only effective and stealthy but also low-cost, showcasing its feasibility across a wide range of AI applications.

Source: IEEE International Conference on Big Data (BigData)

DOI: 10.1109/bigdata62323.2024.10825935

Authors: Kiana Vu, Phung Lai, Truc Nguyen

Institutions: University at Albany, State University of New York, National Laboratory of the Rockies

Language: English

Topic: Adversarial Robustness in Machine Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green