Eigenpatches -- Adversarial Patches from Principal Components
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913616778756096 |
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| author | Bayer, Jens Becker, Stefan Münch, David Arens, Michael |
| author_facet | Bayer, Jens Becker, Stefan Münch, David Arens, Michael |
| contents | Adversarial patches are still a simple yet powerful white box attack that can be used to fool object detectors by suppressing possible detections. The patches of these so-called evasion attacks are computational expensive to produce and require full access to the attacked detector. This paper addresses the problem of computational expensiveness by analyzing 375 generated patches, calculating the principal components of these and show, that linear combinations of the resulting "eigenpatches" can be used to fool object detections successfully. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_10963 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Eigenpatches -- Adversarial Patches from Principal Components Bayer, Jens Becker, Stefan Münch, David Arens, Michael Computer Vision and Pattern Recognition Adversarial patches are still a simple yet powerful white box attack that can be used to fool object detectors by suppressing possible detections. The patches of these so-called evasion attacks are computational expensive to produce and require full access to the attacked detector. This paper addresses the problem of computational expensiveness by analyzing 375 generated patches, calculating the principal components of these and show, that linear combinations of the resulting "eigenpatches" can be used to fool object detections successfully. |
| title | Eigenpatches -- Adversarial Patches from Principal Components |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2306.10963 |