Sparse patches adversarial attacks via extrapolating point-wise information
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929603859185664 |
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| author | Nemcovsky, Yaniv Mendelson, Avi Baskin, Chaim |
| author_facet | Nemcovsky, Yaniv Mendelson, Avi Baskin, Chaim |
| contents | Sparse and patch adversarial attacks were previously shown to be applicable in realistic settings and are considered a security risk to autonomous systems. Sparse adversarial perturbations constitute a setting in which the adversarial perturbations are limited to affecting a relatively small number of points in the input. Patch adversarial attacks denote the setting where the sparse attacks are limited to a given structure, i.e., sparse patches with a given shape and number. However, previous patch adversarial attacks do not simultaneously optimize multiple patches' locations and perturbations. This work suggests a novel approach for sparse patches adversarial attacks via point-wise trimming dense adversarial perturbations. Our approach enables simultaneous optimization of multiple sparse patches' locations and perturbations for any given number and shape. Moreover, our approach is also applicable for standard sparse adversarial attacks, where we show that it significantly improves the state-of-the-art over multiple extensive settings. A reference implementation of the proposed method and the reported experiments is provided at \url{https://github.com/yanemcovsky/SparsePatches.git} |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16162 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sparse patches adversarial attacks via extrapolating point-wise information Nemcovsky, Yaniv Mendelson, Avi Baskin, Chaim Computer Vision and Pattern Recognition Machine Learning Sparse and patch adversarial attacks were previously shown to be applicable in realistic settings and are considered a security risk to autonomous systems. Sparse adversarial perturbations constitute a setting in which the adversarial perturbations are limited to affecting a relatively small number of points in the input. Patch adversarial attacks denote the setting where the sparse attacks are limited to a given structure, i.e., sparse patches with a given shape and number. However, previous patch adversarial attacks do not simultaneously optimize multiple patches' locations and perturbations. This work suggests a novel approach for sparse patches adversarial attacks via point-wise trimming dense adversarial perturbations. Our approach enables simultaneous optimization of multiple sparse patches' locations and perturbations for any given number and shape. Moreover, our approach is also applicable for standard sparse adversarial attacks, where we show that it significantly improves the state-of-the-art over multiple extensive settings. A reference implementation of the proposed method and the reported experiments is provided at \url{https://github.com/yanemcovsky/SparsePatches.git} |
| title | Sparse patches adversarial attacks via extrapolating point-wise information |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2411.16162 |