Open Access
ARTICLE
Adversarial Attacks on License Plate Recognition Systems
Zhaoquan Gu1, Yu Su1, Chenwei Liu1, Yinyu Lyu1, Yunxiang Jian1, Hao Li2, Zhen Cao3, Le Wang1, *
1 Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China.
2 Da Hengqin Science and Technology Development Company, Ltd., Zhuhai, 519000, China.
3 Department of Computer Science, Rice University, Houston, TX 77025, USA.
* Corresponding Author: Le Wang. Email: .
Computers, Materials & Continua 2020, 65(2), 1437-1452. https://doi.org/10.32604/cmc.2020.011834
Received 31 May 2020; Accepted 16 June 2020; Issue published 20 August 2020
Abstract
The license plate recognition system (LPRS) has been widely adopted in daily
life due to its efficiency and high accuracy. Deep neural networks are commonly used in
the LPRS to improve the recognition accuracy. However, researchers have found that
deep neural networks have their own security problems that may lead to unexpected
results. Specifically, they can be easily attacked by the adversarial examples that are
generated by adding small perturbations to the original images, resulting in incorrect
license plate recognition. There are some classic methods to generate adversarial
examples, but they cannot be adopted on LPRS directly. In this paper, we modify some
classic methods to generate adversarial examples that could mislead the LPRS. We
conduct extensive evaluations on the HyperLPR system and the results show that the
system could be easily attacked by such adversarial examples. In addition, we show that
the generated images could also attack the black-box systems; we show some examples
that the Baidu LPR system also makes incorrect recognitions. We hope this paper could
help improve the LPRS by realizing the existence of such adversarial attacks.
Keywords
Cite This Article
APA Style
Gu, Z., Su, Y., Liu, C., Lyu, Y., Jian, Y. et al. (2020). Adversarial attacks on license plate recognition systems. Computers, Materials & Continua, 65(2), 1437-1452. https://doi.org/10.32604/cmc.2020.011834
Vancouver Style
Gu Z, Su Y, Liu C, Lyu Y, Jian Y, Li H, et al. Adversarial attacks on license plate recognition systems. Comput Mater Contin. 2020;65(2):1437-1452 https://doi.org/10.32604/cmc.2020.011834
IEEE Style
Z. Gu et al., "Adversarial Attacks on License Plate Recognition Systems," Comput. Mater. Contin., vol. 65, no. 2, pp. 1437-1452. 2020. https://doi.org/10.32604/cmc.2020.011834
Citations