Deep Learning based Effective Steganalysis
John Babu G1, Sridevi Rangu2
1Mr. John Babu, Ph.D, Department of Computer Science & Engineering, JNTUH College of Engineering, Hyderabad (Telangana), India.
2Dr. Sridevi Rangu, Professor and Head, Department of Computer Science & Engineering, JNTUH College of Engineering, Hyderabad (Telangana), India.
Manuscript received on 25 February 2020 | Revised Manuscript received on 05 March 2020 | Manuscript Published on 15 March 2020 | PP: 82-85 | Volume-9 Issue-4S2 March 2020 | Retrieval Number: D10040394S220/2020©BEIESP | DOI: 10.35940/ijitee.D1004.0394S220
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: There is an evident paradigm shift in steganalysis techniques with discovery of deep learning networks. As steganalysis is a classification task, it is done by machine learning classifiers and ensembles of them. But with the proliferation of deep learning and Convolutional Neural Networks in many areas, the performance of steganalysis techniques have jumped up to a another high, because of the application of Convolutional Neural Networks. The traditional steganalysis techniques consists two important steps, i.e., feature extraction and classification; where as deep learning networks learn the features automatically, eliminating the need of extraction of handcrafted features. Because of this feature CNNs were highly successful in image recognition and image classification techniques. In addition to that, feature extraction and classification are combined together in deep learning hence classification would be more effective because of the learning of the features which are really important for classification. But in Steganalysis the task is to detect very subtle and weak noise created by the hidden data with steganography techniques. We have designed a deep CNN architecture customized for steganalysis task based on existing residual neural networks frame. We have introduced a descriptor to capture the inter pixel dependencies and which acts as an indicator for weightage of a particular feature maps. Thus the classifier can give more weightage to effective feature maps instead of treating all the feature maps equally. We have also used a gating mechanism by using sigmoid function after nonlinear activation function sandwiched between two fully connected layers. This enhancement to the existing deep residual neural networks has given better results in terms of error detection rate compared to the other deep learning based steganalysis techniques.
Keywords: Classification, Convolutional Neural Networks, Deep Learning, Steganalysis.
Scope of the Article: Deep Learning