mlsanityData to 7 Oct 2026

Algebraic Adversarial Attacks on Integrated Gradients

arXiv 2407.16233 ↗First posted 23 Jul 2024cs.LG math.GRcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 11 of its 16 references that are in the map. First posted on arXiv 23 Jul 2024.

Builds on

11 of 16 references.

References · 11

  1. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  2. 2017Axiomatic Attribution for Deep Networkscited by 3,350
  3. 2017SmoothGrad: removing noise by adding noisecited by 1,097
  4. 2017Interpretation of Neural Networks is Fragilecited by 443
  5. 2019A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAIcited by 403
  6. 2019Explanations can be manipulated and geometry is to blamecited by 215
  7. 2019"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanationscited by 111
  8. 2020The Unreasonable Effectiveness of Deep Learning in Artificial Intelligencecited by 57
  9. 2023Adversarial attacks and defenses in explainable artificial intelligence: A surveycited by 47
  10. 2022Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Researchcited by 24
  11. 2024Probabilistic Lipschitzness and the Stable Rank for Comparing Explanation Modelscited by 4

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Cited by year

Citing papers per year: 2024: 1; 2025: 3.

4 papers.

  1. 2024Derivative-Free Diffusion Manifold-Constrained Gradient for Unified XAIcited by 2
  2. 2025Riemannian Integrated Gradients: A Geometric View of Explainable AIcited by 2
  3. 2025Algebraic Adversarial Attacks on Explainability Modelscited by 0
  4. 2025Graph-based Integrated Gradients for Explaining Graph Neural Networkscited by 0