Passive steganalysis based on higher order image statistics of curvelet transform

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.

A Survey on Digital Image Steganography and Steganalysis

“…However, since the image-embedding method is typically unknown to steganalyst, many researches focused on the design of a blind steganalsysis algorithm to detect the presence of steganography independent of the steganography algorithm used. In Geetha's work they employed the higher order statistical features that were collected from a new transform domain, i.e., curvelet transform domain [12]. This work is typically a novel and first attempt of employing these features for Steganalysis.…”