对 "基于人工神经网络的更快更稳健的图像加密技术与改进的 SSIM "的更正

IF 3.4 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Access Pub Date : 2024-10-01 DOI:10.1109/ACCESS.2024.3464189

Asisa Kumar Panigrahy;Shima Ramesh Maniyath;Mithileysh Sathiyanarayanan;Mohan Dholvan;T. Ramaswamy;Sudheer Hanumanthakari;N. Arun Vignesh;S. Kanithan;Raghunandan Swain

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介绍对论文 "一种基于人工神经网络的更快更稳健的图像加密技术与改进的 SSIM "的更正。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Corrections to “A Faster and Robust Artificial Neural Network Based Image Encryption Technique With Improved SSIM”

Presents corrections to the paper, Corrections to “A Faster and Robust Artificial Neural Network Based Image Encryption Technique With Improved SSIM”.

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来源期刊

IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

9.80

自引率

7.70%

发文量

6673

审稿时长

6 weeks

期刊介绍： IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest. IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on: Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals. Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering. Development of new or improved fabrication or manufacturing techniques. Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.