Zhaoquan Gu1, Yu Su1, Chenwei Liu1, Yinyu Lyu1, Yunxiang Jian1, Hao Li2, Zhen Cao3, Le Wang1, *
CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1437-1452, 2020, DOI:10.32604/cmc.2020.011834
- 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 More >
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