# Algebraic Adversarial Attacks on Integrated Gradients

arXiv: https://arxiv.org/abs/2407.16233 · HTML: https://mlsanity.com/paper/2407.16233/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 16 references that are in the map.
- First posted on arXiv 23 Jul 2024.

## Builds on (most cited first, 11 of 11)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [SmoothGrad: removing noise by adding noise](https://mlsanity.com/paper/1706.03825/) (2017)
- [Interpretation of Neural Networks is Fragile](https://mlsanity.com/paper/1710.10547/) (2017)
- [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019)
- [Explanations can be manipulated and geometry is to blame](https://mlsanity.com/paper/1906.07983/) (2019)
- ["How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations](https://mlsanity.com/paper/1911.06473/) (2019)
- [The Unreasonable Effectiveness of Deep Learning in Artificial Intelligence](https://mlsanity.com/paper/2002.04806/) (2020)
- [Adversarial attacks and defenses in explainable artificial intelligence: A survey](https://mlsanity.com/paper/2306.06123/) (2023)
- [Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research](https://mlsanity.com/paper/2208.14937/) (2022)
- [Probabilistic Lipschitzness and the Stable Rank for Comparing Explanation Models](https://mlsanity.com/paper/2402.18863/) (2024)

## Built on by (most cited first, 4 of 4)

- [Derivative-Free Diffusion Manifold-Constrained Gradient for Unified XAI](https://mlsanity.com/paper/2411.15265/) (2024)
- [Riemannian Integrated Gradients: A Geometric View of Explainable AI](https://mlsanity.com/paper/2503.00892/) (2025)
- [Algebraic Adversarial Attacks on Explainability Models](https://mlsanity.com/paper/2503.12683/) (2025)
- [Graph-based Integrated Gradients for Explaining Graph Neural Networks](https://mlsanity.com/paper/2509.07648/) (2025)

## Abstract (arXiv)

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.