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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2407.16233 |
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| _version_ | 1866913708956975104 |
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| author | Simpson, Lachlan Costanza, Federico Millar, Kyle Cheng, Adriel Lim, Cheng-Chew Chew, Hong Gunn |
| author_facet | Simpson, Lachlan Costanza, Federico Millar, Kyle Cheng, Adriel Lim, Cheng-Chew Chew, Hong Gunn |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_16233 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Algebraic Adversarial Attacks on Integrated Gradients Simpson, Lachlan Costanza, Federico Millar, Kyle Cheng, Adriel Lim, Cheng-Chew Chew, Hong Gunn Machine Learning Group Theory 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. |
| title | Algebraic Adversarial Attacks on Integrated Gradients |
| topic | Machine Learning Group Theory |
| url | https://arxiv.org/abs/2407.16233 |