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A Comparative Review of Various Machine Learning Approaches for Improving the Performance of Stego Anomaly Detection

A Comparative Review of Various Machine Learning Approaches for Improving the Performance of Stego Anomaly Detection
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Author(s): Hemalatha Jeyaprakash (Thiagarajar College of Engineering, India), KavithaDevi M. K. (Thiagarajar College of Engineering, India) and Geetha S. (VIT University, India)
Copyright: 2018
Pages: 21
Source title: Handbook of Research on Network Forensics and Analysis Techniques
Source Author(s)/Editor(s): Gulshan Shrivastava (National Institute of Technology Patna, India), Prabhat Kumar (National Institute of Technology Patna, India), B. B. Gupta (National Institute of Technology Kurukshetra, India), Suman Bala (Orange Labs, France) and Nilanjan Dey (Department of Information Technology, Techno India College of Technology, Kolkata, India)
DOI: 10.4018/978-1-5225-4100-4.ch019

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Abstract

In recent years, steganalyzers are intelligently detecting the stego images with high detection rate using high dimensional cover representation. And so the steganographers are working towards this issue to protect the cover element dependency and to protect the detection of hiding secret messages. Any steganalysis algorithm may achieve its success in two ways: 1) extracting the most sensitive features to expose the footprints of message hiding; 2) designing or building an effective classifier engine to favorably detect the stego images through learning all the stego sensitive features. In this chapter, the authors improve the stego anomaly detection using the second approach. This chapter presents a comparative review of application of the machine learning tools for steganalysis problem and recommends the best classifier that produces a superior detection rate.

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