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Research on image steganography analysis based on deep learning
تحقیق در مورد تجزیه و تحلیل استگانوگرافی تصویر بر اساس یادگیری عمیق-2019 Although steganalysis has developed rapidly in recent years, it still faces many difficulties and challenges.
Based on the theory of in-depth learning method and image-based general steganalysis, this paper makes
a deep study of the hot and difficult problem of steganalysis feature expression, and tries to establish a
new steganalysis paradigm from the idea of feature learning. The main contributions of this paper are as
follows: 1. An innovative steganalysis paradigm based on in-depth learning is proposed. Based on the
representative deep learning method CNN, the model is designed and adjusted according to the characteristics
of steganalysis, which makes the proposed model more effective in capturing the statistical characteristics
such as neighborhood correlation. 2. A steganalysis feature learning method based on global
information constraints is proposed. Based on the previous research of steganalysis method based on
CNN, this work focuses on the importance of global information in steganalysis feature expression. 3.
A feature learning method for low embedding rate steganalysis is proposed. 4. A general steganalysis
method for multi-class steganography is proposed. The ultimate goal of general steganalysis is to construct
steganalysis detectors without distinguishing specific types of steganalysis algorithms Keywords: Steganalysis | Steganography | Feature learning | Deep learning | Convolutional neural network | Transfer learning | Multitask learning |
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