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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2408.00783 |
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| _version_ | 1866913455780397056 |
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| author | Otte, Clemens Yang, Yinchong Oswan, Danny Benlin |
| author_facet | Otte, Clemens Yang, Yinchong Oswan, Danny Benlin |
| contents | The paper proposes a new testing approach for Deep Neural Networks (DNN) using gradient-free optimization to find perturbation chains that successfully falsify the tested DNN, going beyond existing grid-based or combinatorial testing. Applying it to an image segmentation task of detecting railway tracks in images, we demonstrate that the approach can successfully identify weaknesses of the tested DNN regarding particular combinations of common perturbations (e.g., rain, fog, blur, noise) on specific clusters of test images. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00783 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-driven Verification of DNNs for Object Recognition Otte, Clemens Yang, Yinchong Oswan, Danny Benlin Computer Vision and Pattern Recognition The paper proposes a new testing approach for Deep Neural Networks (DNN) using gradient-free optimization to find perturbation chains that successfully falsify the tested DNN, going beyond existing grid-based or combinatorial testing. Applying it to an image segmentation task of detecting railway tracks in images, we demonstrate that the approach can successfully identify weaknesses of the tested DNN regarding particular combinations of common perturbations (e.g., rain, fog, blur, noise) on specific clusters of test images. |
| title | Data-driven Verification of DNNs for Object Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.00783 |