Historically, steganographic schemes were designed in a way to preserve image statistics or steganalytic features. Since most of the state-of-the-art steganalytic methods employ a machine learning (ML) based classifier, it is reasonable to consider countering steganalysis by trying to fool the ML classifiers. However, simply applying perturbations on stego images as adversarial examples may lea...


 A hybrid steganographic technique has been proposed to enlarge the size of secret message and hide several images in one cover image. Initially, were compressed using JPEG 2000 before embedding them Second least significant bit plane image was entirely replaced by while tri-ways-pixel-value-differencing (TPVD) used remaining seven-bit The quality stego enhanced residual coding (RIC). div...

Steganography is the act of hiding a message inside another message in such a way that can only be detected by its intended recipient. Naturally, there are security agents who would like to fight these data hiding systems by steganalysis, i.e. discovering covered messages and rendering them useless. There is currently no steganography system which can resist all steganalysis attacks. The most n...

Our recently introduced JPEG steganographic method called Yet Another Steganographic Scheme (YASS) can resist blind steganalysis by embedding data in the discrete cosine transform (DCT) domain in randomly chosen image blocks. To maximize the embedding rate for a given image and a specified attack channel, the redundancy factor used by the repeat-accumulate (RA) code based error correction frame...

In this paper we analyse Stegdetect, one of the well-known image steganalysis tools, to study its false positive rate. In doing so, we process more than 40,000 images randomly downloaded from the Internet using Google images, together with 25,000 images from the ASIRRA (Animal Species Image Recognition for Restricting Access) public corpus. The aim of this study is to help digital forensic anal...

Background: Generic Steganalysis proves to be a boon when there is a suspicion of covert channels with no other information regarding stego images. With the advent of sophisticated steganographic techniques, the process becomes tough as the hidden data is very meager and leaves undecipherable artifacts. A Universal, Blind and Statistical Steganalyzer needs to be more generalized as it encounter...

Calibration and higher order statistics (HOS) are standard components of many image steganalysis systems. These techniques have not yet found adequate attention in audio steganalysis context. Specifically, most of current works are either non-calibrated or only based on noise removal approach. This paper aims to fill these gaps by proposing a new set of calibrated features based on re-embedding...

WAM steganalysis is a feature-based classifier for detecting LSB matching steganography, presented in 2006 by Goljan et al. and demonstrated to be sensitive even to small payloads. This paper makes three contributions to the development of the WAM method. First, we benchmark some variants of WAM in a number of sets of cover images, and we are able to quantify the significance of differences in ...

3D steganalysis aims to identify subtle invisible changes produced in graphical objects through digital watermarking or steganography. Sets of statistical representations of 3D features, extracted from both cover and stego 3D mesh objects, are used as inputs into machine learning classifiers in order to decide whether any information was hidden in the given graphical object. According to previo...

Image steganography is a technique of embedding secret message into a digital image to securely send the information. In contrast, steganalysis focuses on detecting the presence of secret messages hidden by steganography. The modern approach in steganalysis is based on supervised learning where the training set must include the steganographic and natural image features. But if a new method of s...