Title: Analysis of electronic bill authentication and security storage performance utilising machine learning algorithm
Authors: Jingcheng Tian; Lingbo Yang; Yutao Zhang; Wei Qian
Addresses: Jilin University Information Technologies Co., Ltd., Beijing City 100086, China ' Jilin University Information Technologies Co., Ltd., Beijing City 100086, China ' Jilin University Information Technologies Co., Ltd., Changchun, Jilin City 130012, China ' Jilin University Information Technologies Co., Ltd., Beijing City 100086, China
Abstract: The study aims to ensure the security and authentication efficiency of the bill image, and the encryption and decryption methods and security protection of the electronic bill are studied in the experiment. First, aiming at the not high traditional electronic bill security performance, a method is proposed, namely, embedding a watermark into a binary image with edge information. Second, aiming at the weak compression robust character of electronic bill image, the method of chaotic encryption of digital watermark through wavelet coefficient matrix algorithm is proposed to be combined with the binary sequence. Finally, the deep learning algorithm combined with the convolution algorithm can detect the quality of electronic bill watermark images. The results show that the method of embedding watermark with edge information effectively has improved the confidentiality of electronic bills. The method of chaotic encryption of digital watermarks by wavelet coefficient matrix algorithm combined with binary sequence has improved the anti-compression ability of digital watermarks. The multi-watermark encryption method has enhanced the tamper-proof ability of electronic bills and has improved the security performance of bills, and the deep convolution algorithm has improved the security and efficiency of electronic bill processing.
Keywords: electronic bills; machine learning algorithms; deep convolution algorithm; multiple watermark encryption; binary image; chaotic encryption.
International Journal of Grid and Utility Computing, 2022 Vol.13 No.1, pp.30 - 39
Received: 02 Jan 2021
Accepted: 06 May 2021
Published online: 11 Mar 2022 *