Study on machine learning approaches for stego anomaly detection

Hemalatha J; Kavitha Devi M K

doi:10.4172/2168-9695-C1-009

Study on machine learning approaches for stego anomaly detection

3^rd International conference on Artificial Intelligence & Robotics

June 28-29, 2017 San Diego, USA

Hemalatha J and Kavitha Devi M K

Thiagarajar College of Engineering, India

Posters & Accepted Abstracts: Adv Robot Autom

Abstract :

Due to the availability of high dimensional cover representation, recent steganographers are frustrating to preserve the dependency among covers elements and accordingly prevent detection using best steganalyzers. The success of steganalysis is originated by two ways. Initially steganalyzers should extract and find the useful features among the thousands of features are available. Later steganalyzers needs a best machine learning algorithms/tool to effectively learn all the useful features and give more promising detection accuracy. This chapter focuses on the further study of machine learning tool/algorithms used by the steganalyzers in the literature and its promising accuracy in detecting the stego images. As a final point to claim and argue the best machine learning algorithms are giving the most promising stego image detection.