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Abstrakt
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
Cytowania
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2
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0
Web of Science
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3
Scopus
Autorzy (5)
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Juan Wang
- Chengdu University, China
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Lei Shi
- Chengdu University, China
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Yang Zhao
- Chengdu University, China
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Haoxi Zhang
- Chengdu University, China
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Informacje szczegółowe
- Kategoria:
- Publikacja w czasopiśmie
- Typ:
- artykuły w czasopismach
- Opublikowano w:
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MULTIMEDIA TOOLS AND APPLICATIONS
ISSN: 1380-7501 - Język:
- angielski
- Rok wydania:
- 2022
- Opis bibliograficzny:
- Wang J., Shi L., Zhao Y., Zhang H., Szczerbicki E.: Adversarial attack algorithm for traffic sign recognition// MULTIMEDIA TOOLS AND APPLICATIONS -, (2022),
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1007/s11042-022-14067-5
- Źródła finansowania:
-
- Publikacja bezkosztowa
- Weryfikacja:
- Politechnika Gdańska
wyświetlono 108 razy
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