Passive steganalysis based on higher order image statistics of curvelet transform

Authors:
S. Geetha;Siva S. Sivatha Sindhu;N. Kamaraj
Affiliations:
Department of Information Technology, Thiagarajar College of Engineering, Madurai, India 625 015;Department of Information Technology, Thiagarajar College of Engineering, Madurai, India 625 015;Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai, India 625 015
Venue:
International Journal of Automation and Computing
Year:
2010

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Abstract

Steganographic techniques accomplish covert communication by embedding secret messages into innocuous digital images in ways that are imperceptible to the human eye. This paper presents a novel passive steganalysis strategy in which the task is approached as a pattern classification problem. A critical part of the steganalyser design depends on the selection of informative features. This paper is aimed at proposing a novel attack with improved performance indices with the following implications: 1) employing higher order statistics from a curvelet sub-band image representation that offers better discrimination ability for detecting stego anomalies in images, as compared to other conventional wavelet transforms; 2) increasing the sensitivity and specificity of the system by the feature reduction phase; 3) realizing the system using an efficient classification engine, a neuro-C4.5 classifier, which provides better classification rate. An extensive experimental evaluation on a database containing 5600 clean and stego images shows that the proposed scheme is a state-of-the-art steganalyser that outperforms other previous steganalytic methods.