Passive steganalysis based on higher order image statistics of curvelet transform

摘要: 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 which task is approached as pattern classification problem. A critical part of steganalyser design depends on selection informative features. aimed at proposing attack with improved performance indices following implications: 1) employing higher order statistics from curvelet sub-band image representation offers better discrimination ability for detecting stego anomalies images, compared other conventional wavelet transforms; 2) increasing sensitivity and specificity system feature reduction phase; 3) realizing using an efficient engine, neuro-C4.5 classifier, provides rate. An extensive experimental evaluation database containing 5600 clean shows proposed scheme state-of-the-art outperforms previous steganalytic methods.