Multi-level loss object tracking adversarial attack method based on spatial perception
In order to solve the problem that it is difficult for the existing adversarial disturbance techniques to effectively reduce the discrimination ability of the trackers and make the trajectory deviation rapidly, an effective object tracking adversarial attack method was proposed.First, deception loss...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2021-11-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021208/ |
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| _version_ | 1841539235701587968 |
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| author | Xu CHENG Yingying WANG Nianjie ZHANG Zhangjie FU Beijing CHEN Guoying ZHAO |
| author_facet | Xu CHENG Yingying WANG Nianjie ZHANG Zhangjie FU Beijing CHEN Guoying ZHAO |
| author_sort | Xu CHENG |
| collection | DOAJ |
| description | In order to solve the problem that it is difficult for the existing adversarial disturbance techniques to effectively reduce the discrimination ability of the trackers and make the trajectory deviation rapidly, an effective object tracking adversarial attack method was proposed.First, deception loss, drift loss and attention mechanism-based loss was designed to jointly train generator based on the consideration of the high-level categories and the low-level features.Then, the clean image was sent to the trained generator to generate the adversarial samples that were used to interfere with the object trackers, which made the object trajectory deviation and reduced the tracking accuracy.Experimental results show that the proposed method achieves 54% reduction in success rate and 70% reduction in accuracy on OTB dataset, which can attack the object of tracking quickly in complex scenes. |
| format | Article |
| id | doaj-art-d43278b2f9e14549b8b7efad704d86c6 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2021-11-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-d43278b2f9e14549b8b7efad704d86c62025-01-14T07:23:13ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2021-11-014224225459746402Multi-level loss object tracking adversarial attack method based on spatial perceptionXu CHENGYingying WANGNianjie ZHANGZhangjie FUBeijing CHENGuoying ZHAOIn order to solve the problem that it is difficult for the existing adversarial disturbance techniques to effectively reduce the discrimination ability of the trackers and make the trajectory deviation rapidly, an effective object tracking adversarial attack method was proposed.First, deception loss, drift loss and attention mechanism-based loss was designed to jointly train generator based on the consideration of the high-level categories and the low-level features.Then, the clean image was sent to the trained generator to generate the adversarial samples that were used to interfere with the object trackers, which made the object trajectory deviation and reduced the tracking accuracy.Experimental results show that the proposed method achieves 54% reduction in success rate and 70% reduction in accuracy on OTB dataset, which can attack the object of tracking quickly in complex scenes.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021208/video surveillancenetwork securityadversarial attackdeep learningobject tracking |
| spellingShingle | Xu CHENG Yingying WANG Nianjie ZHANG Zhangjie FU Beijing CHEN Guoying ZHAO Multi-level loss object tracking adversarial attack method based on spatial perception Tongxin xuebao video surveillance network security adversarial attack deep learning object tracking |
| title | Multi-level loss object tracking adversarial attack method based on spatial perception |
| title_full | Multi-level loss object tracking adversarial attack method based on spatial perception |
| title_fullStr | Multi-level loss object tracking adversarial attack method based on spatial perception |
| title_full_unstemmed | Multi-level loss object tracking adversarial attack method based on spatial perception |
| title_short | Multi-level loss object tracking adversarial attack method based on spatial perception |
| title_sort | multi level loss object tracking adversarial attack method based on spatial perception |
| topic | video surveillance network security adversarial attack deep learning object tracking |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021208/ |
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