Efficient steganalysis using convolutional auto encoder network to ensure original image quality
| Autor: | Srinivasa Reddy Konda, Sudharshan Reddy Chidirala, Mallikarjuna Reddy Ayaluri, K. Sudheer Reddy |
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| Rok vydání: | 2021 |
| Předmět: |
Non Gaussian noise
General Computer Science Computer science Word error rate 02 engineering and technology Deep neural network Convolutional neural network lcsh:QA75.5-76.95 symbols.namesake 0202 electrical engineering electronic engineering information engineering Image quality Error cost Steganalysis Artificial neural network business.industry Deep learning Security and Privacy Convolutional auto encoder deep learning framework Auto encoder Pattern recognition 021001 nanoscience & nanotechnology Autoencoder Gaussian noise Cryptography symbols 020201 artificial intelligence & image processing lcsh:Electronic computers. Computer science Artificial intelligence Noise (video) 0210 nano-technology business |
| Zdroj: | PeerJ Computer Science, Vol 7, p e356 (2021) PeerJ Computer Science |
| ISSN: | 2376-5992 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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