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. 2021 Feb 16:7:e356.
doi: 10.7717/peerj-cs.356. eCollection 2021.

Efficient steganalysis using convolutional auto encoder network to ensure original image quality

Mallikarjuna Reddy Ayaluri  1 , Sudheer Reddy K  2 , Srinivasa Reddy Konda  3 , Sudharshan Reddy Chidirala  4
Affiliations

Efficient steganalysis using convolutional auto encoder network to ensure original image quality

Mallikarjuna Reddy Ayaluri et al. PeerJ Comput Sci. .

Abstract

Steganalysis is the process of analyzing and predicting the presence of hidden information in images. Steganalysis would be most useful to predict whether the received images contain useful information. However, it is more difficult to predict the hidden information in images which is computationally difficult. In the existing research method, this is resolved by introducing the deep learning approach which attempts to perform steganalysis tasks in effectively. However, this research method does not concentrate the noises present in the images. It might increase the computational overhead where the error cost adjustment would require more iteration. This is resolved in the proposed research technique by introducing the novel research method called Non-Gaussian Noise Aware Auto Encoder Convolutional Neural Network (NGN-AEDNN). Classification technique provides a more flexible way for steganalysis where the multiple features present in the environment would lead to an inaccurate prediction rate. Here, learning accuracy is improved by introducing noise removal techniques before performing a learning task. Non-Gaussian Noise Removal technique is utilized to remove the noises before learning. Also, Gaussian noise removal is applied at every iteration of the neural network to adjust the error rate without the involvement of noisy features. This proposed work can ensure efficient steganalysis by accurate learning task. Matlab has been employed to implement the method by performing simulations from which it is proved that the proposed research technique NGN-AEDNN can ensure the efficient steganalysis outcome with the reduced computational overhead when compared with the existing methods.

Keywords: Auto encoder; Convolutional auto encoder deep learning framework; Deep neural network; Error cost; Image quality; Non Gaussian noise; Steganalysis.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1. Processing flow of proposed work.
Figure 2
Figure 2. Comparison evaluation graph.
See this image and copyright information in PMC

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