Open Access

ARTICLE

Image Steganography Using Deep Neural Networks

by Kavitha Chinniyan^*, Thamil Vani Samiyappan, Aishvarya Gopu, Narmatha Ramasamy

Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, 641004, India

* Corresponding Author: Kavitha Chinniyan. Email:

Intelligent Automation & Soft Computing 2022, 34(3), 1877-1891. https://doi.org/10.32604/iasc.2022.027274

Received 13 January 2022; Accepted 20 February 2022; Issue published 25 May 2022

Abstract

Steganography is the technique of hiding secret data within ordinary data by modifying pixel values which appear normal to a casual observer. Steganography which is similar to cryptography helps in secret communication. The cryptography method focuses on the authenticity and integrity of the messages by hiding the contents of the messages. Sometimes, it is not only just enough to encrypt the message but also essential to hide the existence of the message itself. As this avoids misuse of data, this kind of encryption is less suspicious and does not catch attention. To achieve this, Stacked Autoencoder model is developed which initially compresses and encodes the data effectively and which finally decodes the data back from the compressed encoded representation to a representation that is more similar to the original input. The secret data is encrypted using Elliptic Curve Cryptography algorithm and transformed into an image before it is encoded by the model which combines the cover and the secret image. The cover and the secret image produce a container image which after decoding and decrypting gives the secret data. The proposed work consists of multiple networks which are trained with Flickr Image Dataset and results in Secret Image with a Loss of 4% and Container Image with a Loss of 14%.

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