The haze situation will seriously affect the quality of license plate recognition and reduce the performance of the visual processing algorithm. In order to improve the quality of haze pictures, a license plate recognition algorithm based on haze weather is proposed in this paper. The algorithm in this paper mainly consists of two parts: The first part is MPGAN image dehazing, which uses a generative adversarial network to dehaze the image, and combines multi-scale convolution and perceptual loss. Multi-scale convolution is conducive to better feature extraction. The perceptual loss makes up for the shortcoming that the mean square error (MSE) is greatly affected by outliers; the second part is to recognize the license plate, first we use YOLOv3 to locate the license plate, the STN network corrects the license plate, and finally enters the improved LPRNet network to get license plate information. Experimental results show that the dehazing model proposed in this paper achieves good results, and the evaluation indicators PSNR and SSIM are better than other representative algorithms. After comparing the license plate recognition algorithm with the LPRNet algorithm, the average accuracy rate can reach 93.9%.

Impossible differential attack (IDA) uses impossible differential characteristics extracted from enough plaintext pairs to retrieve subkeys of the first and the last several rounds of AES. In this paper, a general IDA on 7-round AES is proposed. Such attack takes the number of all-zero columns of the 7th and the 6th round as parameters (α,β). And a trade-off relation between the number of plaintexts and times of encryptions in the process of the attack is derived, which makes only some values of (α,β) allowed in the attack for different key length.