Algebraic Adversarial Attacks on Integrated Gradients
<|reference_start|>Algebraic Adversarial Attacks on Integrated Gradients: 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.<|reference_end|>
@article{simpson2024algebraic,
    title={Algebraic Adversarial Attacks on Integrated Gradients},
    author={Lachlan Simpson, Federico Costanza, Kyle Millar, Adriel Cheng,
  Cheng-Chew Lim, Hong Gunn Chew},
    journal={arXiv preprint arXiv:2407.16233},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16233},
    primaryClass={cs.LG math.GR}
}