CAJ | 학술논문

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.