Open AccessConvolution Neural Networks for Blind Image Steganalysis: A Comprehensive Study
Author(s) -
Hanaa Mohsin Ahmed,
Halah Hasan Mahmoud
Publication year - 2019
Publication title -
journal of al-qadisiyah for computer science and mathematics
Language(s) - English
Resource type - Journals
eISSN - 2521-3504
pISSN - 2074-0204
DOI - 10.29304/jqcm.2019.11.2.573
Subject(s) - steganalysis , computer science , steganography , convolution (computer science) , artificial intelligence , convolutional neural network , payload (computing) , variety (cybernetics) , image (mathematics) , pattern recognition (psychology) , cover (algebra) , identification (biology) , artificial neural network , computer security , engineering , biology , network packet , mechanical engineering , botany
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.
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