Open AccessA fast and effective image steganalysis model based on convolutional neural network
Author(s) -
HU Feng-song,
Rong Xu,
Zhekun Cheng
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1861/1/012074
Subject(s) - steganalysis , pooling , computer science , convolutional neural network , steganography , embedding , artificial intelligence , image (mathematics) , deep learning , layer (electronics) , pattern recognition (psychology) , artificial neural network , machine learning , chemistry , organic chemistry
In recent years, the performance of deep learning in image steganalysis applications has become more and more outstanding, but at the same time the training time has also greatly increased. Some models need to be trained for several days, and the research efficiency is very low. In this article, we propose an image steganalysis model in spatial domain based on a three-layer convolutional neural network. The model does not use a pooling layer, and uses the global average pooling layer instead of the fully connected layer. Experimental results show that the training time of the model is greatly shortened, and the accuracy of detecting the three steganography algorithms with an embedding rate of 0.4bpp exceeds 85%.