Algebraic Adversarial Attacks on Explainability Models

• URL: http://arxiv.org/abs/2503.12683v1
• Date: Sun, 16 Mar 2025 22:55:02 GMT
• Title: Algebraic Adversarial Attacks on Explainability Models
• Authors: Lachlan Simpson, Federico Costanza, Kyle Millar, Adriel Cheng, Cheng-Chew Lim, Hong Gunn Chew,
• Abstract summary: Algebraic adversarial examples provide a mathematically tractable approach to adversarial examples.<n>We validate our approach on two well-known and one real-world dataset.
• Score: 5.286919475372417
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Classical adversarial attacks are phrased as a constrained optimisation problem. Despite the efficacy of a constrained optimisation approach to adversarial attacks, one cannot trace how an adversarial point was generated. In this work, we propose an algebraic approach to adversarial attacks and study the conditions under which one can generate adversarial examples for post-hoc explainability models. Phrasing neural networks in the framework of geometric deep learning, algebraic adversarial attacks are constructed through analysis of the symmetry groups of neural networks. Algebraic adversarial examples provide a mathematically tractable approach to adversarial examples. We validate our approach of algebraic adversarial examples on two well-known and one real-world dataset.

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