A Vulnerability of Attribution Methods Using Pre-Softmax Scores
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
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| _version_ | 1866909163674664960 |
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| author | Lerma, Miguel Lucas, Mirtha |
| author_facet | Lerma, Miguel Lucas, Mirtha |
| contents | We discuss a vulnerability involving a category of attribution methods used to provide explanations for the outputs of convolutional neural networks working as classifiers. It is known that this type of networks are vulnerable to adversarial attacks, in which imperceptible perturbations of the input may alter the outputs of the model. In contrast, here we focus on effects that small modifications in the model may cause on the attribution method without altering the model outputs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_03305 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Vulnerability of Attribution Methods Using Pre-Softmax Scores Lerma, Miguel Lucas, Mirtha Machine Learning Artificial Intelligence 68T07 I.2.m We discuss a vulnerability involving a category of attribution methods used to provide explanations for the outputs of convolutional neural networks working as classifiers. It is known that this type of networks are vulnerable to adversarial attacks, in which imperceptible perturbations of the input may alter the outputs of the model. In contrast, here we focus on effects that small modifications in the model may cause on the attribution method without altering the model outputs. |
| title | A Vulnerability of Attribution Methods Using Pre-Softmax Scores |
| topic | Machine Learning Artificial Intelligence 68T07 I.2.m |
| url | https://arxiv.org/abs/2307.03305 |