Convolution neural networks for blind image steganalysis : a comprehensive study
Other Title(s)
الشبكات العصبية التلافيفية لتحليل الإخفاء الأعمى في الصورة : دراسة شاملة
Joint Authors
Ahmad, Hana Muhsin
Mahmud, Halah Hasan
Source
al-Qadisiyah Journal for Computer Science and Mathematics
Issue
Vol. 11, Issue 2 (30 Jun. 2019), pp.53-64, 12 p.
Publisher
University of al-Qadisiyah College of computer Science and Information Technology
Publication Date
2019-06-30
Country of Publication
Iraq
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
Recently, Convolution Neural Network is widely applied in Image Classification, Object Detection, Scene labeling, Speech, Natural Language Processing and other fields.
In this comprehensive study a variety of scenarios and efforts are surveyed since 2014 at yet, in order to provide a guide to further improve future researchers what CNN-based blind image steganalysis are presented its architecture, performance and limitations.
Long-standing and important problem in image steganalysis difficulties mainly lie in how to give high accuracy and low payload in stego or cover images for improving performance of the network.
American Psychological Association (APA)
Ahmad, Hana Muhsin& Mahmud, Halah Hasan. 2019. Convolution neural networks for blind image steganalysis : a comprehensive study. al-Qadisiyah Journal for Computer Science and Mathematics،Vol. 11, no. 2, pp.53-64.
https://search.emarefa.net/detail/BIM-883449
Modern Language Association (MLA)
Ahmad, Hana Muhsin& Mahmud, Halah Hasan. Convolution neural networks for blind image steganalysis : a comprehensive study. al-Qadisiyah Journal for Computer Science and Mathematics Vol. 11, no. 2 (2019), pp.53-64.
https://search.emarefa.net/detail/BIM-883449
American Medical Association (AMA)
Ahmad, Hana Muhsin& Mahmud, Halah Hasan. Convolution neural networks for blind image steganalysis : a comprehensive study. al-Qadisiyah Journal for Computer Science and Mathematics. 2019. Vol. 11, no. 2, pp.53-64.
https://search.emarefa.net/detail/BIM-883449
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 63
Record ID
BIM-883449