Adversarial attacks on explainability models have drastic consequences when explanations are used to understand the reasoning of neural networks in safety critical systems. Path methods are one such class of attribution methods susceptible to adversarial attacks. Adversarial learning is typically phrased as a constrained optimisation problem. In this work, we propose algebraic adversarial examples and study the conditions under which one can generate adversarial examples for integrated gradients. Algebraic adversarial examples provide a mathematically tractable approach to adversarial examples.
@article{arxiv.2407.16233,
title = {Algebraic Adversarial Attacks on Integrated Gradients},
author = {Lachlan Simpson and Federico Costanza and Kyle Millar and Adriel Cheng and Cheng-Chew Lim and Hong Gunn Chew},
journal= {arXiv preprint arXiv:2407.16233},
year = {2025}
}
Comments
To appear in the proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC)