IPatch: a remote adversarial patch
Abstract Applications such as autonomous vehicles and medical screening use deep learning models to localize and identify hundreds of objects in a single frame. In the past, it has been shown how an attacker can fool these models by placing an adversarial patch within a scene. However, these patches...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-05-01
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| Series: | Cybersecurity |
| Online Access: | https://doi.org/10.1186/s42400-023-00145-0 |
| _version_ | 1797832035640803328 |
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| author | Yisroel Mirsky |
| author_facet | Yisroel Mirsky |
| author_sort | Yisroel Mirsky |
| collection | DOAJ |
| description | Abstract Applications such as autonomous vehicles and medical screening use deep learning models to localize and identify hundreds of objects in a single frame. In the past, it has been shown how an attacker can fool these models by placing an adversarial patch within a scene. However, these patches must be placed in the target location and do not explicitly alter the semantics elsewhere in the image. In this paper, we introduce a new type of adversarial patch which alters a model’s perception of an image’s semantics. These patches can be placed anywhere within an image to change the classification or semantics of locations far from the patch. We call this new class of adversarial examples ‘remote adversarial patches’ (RAP). We implement our own RAP called IPatch and perform an in-depth analysis on without pixel clipping on image segmentation RAP attacks using five state-of-the-art architectures with eight different encoders on the CamVid street view dataset. Moreover, we demonstrate that the attack can be extended to object recognition models with preliminary results on the popular YOLOv3 model. We found that the patch can change the classification of a remote target region with a success rate of up to 93% on average. |
| first_indexed | 2024-04-09T14:01:16Z |
| format | Article |
| id | doaj.art-990b5f0cacd34ef593f910e8de66544e |
| institution | Directory Open Access Journal |
| issn | 2523-3246 |
| language | English |
| last_indexed | 2024-04-09T14:01:16Z |
| publishDate | 2023-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Cybersecurity |
| spelling | doaj.art-990b5f0cacd34ef593f910e8de66544e2023-05-07T11:16:31ZengSpringerOpenCybersecurity2523-32462023-05-016111910.1186/s42400-023-00145-0IPatch: a remote adversarial patchYisroel Mirsky0Ben-Gurion University, Department of Software and Information Systems EngineeringAbstract Applications such as autonomous vehicles and medical screening use deep learning models to localize and identify hundreds of objects in a single frame. In the past, it has been shown how an attacker can fool these models by placing an adversarial patch within a scene. However, these patches must be placed in the target location and do not explicitly alter the semantics elsewhere in the image. In this paper, we introduce a new type of adversarial patch which alters a model’s perception of an image’s semantics. These patches can be placed anywhere within an image to change the classification or semantics of locations far from the patch. We call this new class of adversarial examples ‘remote adversarial patches’ (RAP). We implement our own RAP called IPatch and perform an in-depth analysis on without pixel clipping on image segmentation RAP attacks using five state-of-the-art architectures with eight different encoders on the CamVid street view dataset. Moreover, we demonstrate that the attack can be extended to object recognition models with preliminary results on the popular YOLOv3 model. We found that the patch can change the classification of a remote target region with a success rate of up to 93% on average.https://doi.org/10.1186/s42400-023-00145-0 |
| spellingShingle | Yisroel Mirsky IPatch: a remote adversarial patch Cybersecurity |
| title | IPatch: a remote adversarial patch |
| title_full | IPatch: a remote adversarial patch |
| title_fullStr | IPatch: a remote adversarial patch |
| title_full_unstemmed | IPatch: a remote adversarial patch |
| title_short | IPatch: a remote adversarial patch |
| title_sort | ipatch a remote adversarial patch |
| url | https://doi.org/10.1186/s42400-023-00145-0 |
| work_keys_str_mv | AT yisroelmirsky ipatcharemoteadversarialpatch |