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Issue title: Soft Computing and Intelligent Systems: Techniques and Applications
Guest editors: Sabu M. Thampi, El-Sayed M. El-Alfy and Ljiljana Trajkovic
Article type: Research Article
Authors: Sarraf, Gaurava; * | Srivatsa, Anirudh Ramesha | Swetha, MSa
Affiliations: [a] Information Science & Engineering, B. M. S. Institute of Technology & Management, Bangalore, India
Correspondence: [*] Corresponding author. Gaurav Sarraf, Information Science & Engineering, B. M. S. Institute Of Technology & Management, Bangalore, India. E-mail: [email protected].
Abstract: With the ever-rising threat to security, multiple industries are always in search of safer communication techniques both in rest and transit. Multiple security institutions agree that any systems security can be modeled around three major concepts: Confidentiality, Availability, and Integrity. We try to reduce the holes in these concepts by developing a Deep Learning based Steganography technique. In our study, we have seen, data compression has to be at the heart of any sound steganography system. In this paper, we have shown that it is possible to compress and encode data efficiently to solve critical problems of steganography. The deep learning technique, which comprises an auto-encoder with Convolutional Neural Network as its building block, not only compresses the secret file but also learns how to hide the compressed data in the cover file efficiently. The proposed techniques can encode secret files of the same size as of cover, or in some sporadic cases, even larger files can be encoded. We have also shown that the same model architecture can theoretically be applied to any file type. Finally, we show that our proposed technique surreptitiously evades all popular steganalysis techniques.
Keywords: Cybersecurity, multimedia steganography, steganalysis, convolutional neural networks, cryptography
DOI: 10.3233/JIFS-189879
Journal: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 5, pp. 5595-5606, 2021
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