Header menu link for other important links
A Comparative Review of Various Machine Learning Approaches for Improving the Performance of Stego Anomaly Detection
Hemalatha Jeyaprakash, KavithaDevi M. K,
Published in IGI Global
Pages: 351 - 371
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.
About the journal
JournalHandbook of Research on Network Forensics and Analysis Techniques Advances in Information Security, Privacy, and Ethics
PublisherIGI Global
Open Access0