Image steganography recognition using artificial neural networks

Ndumiyana, David. (2015) Image steganography recognition using artificial neural networks.

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The problem of protecting information from unauthorised and undesired use is becoming more complex since the production, storage and exchange have become an important function of most modern societies. People graduating from learning institutions, celebrating birthdays, marriages and other achievements in life are photographed and later exchange these photos with families and friends all over the globe. These photo images can be exploited to conceal the existence of embedded malicious messages between communicating parties. Image files are usually stored with the highest degree of precision thereby providing a fertile environment for steganography. In this paper, we propose a supervised machine learning technique to steganalysis for images using lossy compression formats such as JPEG/MPEG and lossless representation, among them GIF. Images used in this investigation are represented as a canvas, the available space inside the picture that can be used to hide messages. We then classify features into clean and stego-containing files before applying machine learning (ML) algorithms to distinguish clean from stego-infected files. The results obtained and reported showed that our ML algorithms are a solution to steganography in both content and compressed image formats. Keywords: Neural network, Steganography, steganalysis, lossless image, image compression

Item Type: Article
Uncontrolled Keywords: Steganography,Steganalysis,Image compression,Neural network
Divisions: Universities > State Universities > Bindura University of Science Education
Depositing User: Mr. Edmore Sibanda
Date Deposited: 14 May 2018 09:04
Last Modified: 14 May 2018 09:04

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