Adversarial attack algorithm for traffic sign recognition
Deep learning suffers from the threat of adversarial attacks, and its defense methods have become a research hotspot. In all applications of deep learning, intelligent driving is an important and promising one, facing serious threat of adversarial attack in the meanwhile. To address the adversarial...
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| Published in: | Multimedia tools and applications Vol. 84; no. 29; pp. 35137 - 35149 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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New York
Springer US
01.09.2025
Springer Nature B.V |
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| ISSN: | 1573-7721, 1380-7501, 1573-7721 |
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| Abstract | Deep learning suffers from the threat of adversarial attacks, and its defense methods have become a research hotspot. In all applications of deep learning, intelligent driving is an important and promising one, facing serious threat of adversarial attack in the meanwhile. To address the adversarial attack, this paper takes the traffic sign recognition as a typical object, for it is the core function of intelligent driving. Considering that the black box attack does not need to know the internal characteristics of the model, it can have more practical value. However, the existing black box attack algorithm has high visit time and low efficiency in attacking sample generation. In this regard, the SimBA algorithm with high efficiency is selected and improved according to the characteristics of traffic signs, named the L-SimBA algorithm. According to the graphic characteristics of traffic signs that are already known, L-SimBA algorithm limits the search subspace consciously and specifies the set of search directions, and that is the core idea of it. By this way, L-SimBA algorithm can generate adversarial samples faster. Experimental comparison shows that in the field of traffic sign recognition, L-SimBA algorithm is better than SimBA algorithm. On the premise of obtaining similar quality adversarial attack samples, the success rate of adversarial measures gets higher, and the number of model visits reduces considerably, thus the attack efficiency of the algorithm improves greatly. |
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| AbstractList | Deep learning suffers from the threat of adversarial attacks, and its defense methods have become a research hotspot. In all applications of deep learning, intelligent driving is an important and promising one, facing serious threat of adversarial attack in the meanwhile. To address the adversarial attack, this paper takes the traffic sign recognition as a typical object, for it is the core function of intelligent driving. Considering that the black box attack does not need to know the internal characteristics of the model, it can have more practical value. However, the existing black box attack algorithm has high visit time and low efficiency in attacking sample generation. In this regard, the SimBA algorithm with high efficiency is selected and improved according to the characteristics of traffic signs, named the L-SimBA algorithm. According to the graphic characteristics of traffic signs that are already known, L-SimBA algorithm limits the search subspace consciously and specifies the set of search directions, and that is the core idea of it. By this way, L-SimBA algorithm can generate adversarial samples faster. Experimental comparison shows that in the field of traffic sign recognition, L-SimBA algorithm is better than SimBA algorithm. On the premise of obtaining similar quality adversarial attack samples, the success rate of adversarial measures gets higher, and the number of model visits reduces considerably, thus the attack efficiency of the algorithm improves greatly. |
| Author | Shi, Lei Zhang, Haoxi Wang, Juan Zhao, Yang Szczerbicki, Edward |
| Author_xml | – sequence: 1 givenname: Juan surname: Wang fullname: Wang, Juan organization: School of Cyberspace Security, Chengdu University of Information Technology, Advanced Cryptography and System Security Key Laboratory of Sichuan Province – sequence: 2 givenname: Lei orcidid: 0000-0002-3787-8103 surname: Shi fullname: Shi, Lei email: sl@cuit.edu.cn organization: School of Cyberspace Security, Chengdu University of Information Technology – sequence: 3 givenname: Yang surname: Zhao fullname: Zhao, Yang organization: School of Cyberspace Security, Chengdu University of Information Technology – sequence: 4 givenname: Haoxi surname: Zhang fullname: Zhang, Haoxi organization: School of Cyberspace Security, Chengdu University of Information Technology, Advanced Cryptography and System Security Key Laboratory of Sichuan Province – sequence: 5 givenname: Edward surname: Szczerbicki fullname: Szczerbicki, Edward organization: Faculty of Management and Economics, Department of management, the Gdansk University of Technology |
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| Cites_doi | 10.1109/TIP.2021.3092822 10.1109/TPAMI.2020.3031625 10.1109/ACCESS.2020.3024149 10.1109/TIFS.2022.3175603 10.1109/TCSII.2020.2980022 10.1109/ACCESS.2021.3124050 10.1109/ACCESS.2022.3174963 10.1109/ACCESS.2021.3092646 10.1109/ACCESS.2021.3138338 10.1109/TIFS.2020.3036801 10.1109/LSP.2021.3106239 10.1109/TIP.2021.3137648 10.1109/LGRS.2022.3184311 10.1109/TR.2022.3161138 |
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| Copyright | The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. |
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| Keywords | Black box Traffic sign recognition Adversarial attack Algorithm security |
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| SubjectTerms | 1231: IoT-driven Computer Vision Technology for Smart Transportation Applications Algorithms Artificial intelligence Black boxes Computer Communication Networks Computer Science Data Structures and Information Theory Deep learning Efficiency Multimedia Information Systems Neural networks Object recognition Signs Speaking Special Purpose and Application-Based Systems Street signs Traffic control Traffic signs Voice recognition |
| Title | Adversarial attack algorithm for traffic sign recognition |
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