Aiming at detecting secret information hidden in a given image using steganographic tools, steganalysis has been of interest since the end of 1990’s. In particular, universal steganalysis, not limited to attacking a specific steganographic tool, is of extensive interests due to its practicality. Recently, splicing detection, another important area in the field of digital forensics has attracted...

Deep learning based image steganalysis has attracted increasing attentions in recent years. Several Convolutional Neural Network (CNN) models have been proposed and achieved state-of-the-art performances on detecting steganography. In this paper, we explore an important technique in deep learning, the batch normalization, for the task of image steganalysis. Different from natural image classifi...

Modern steganalysis is a combination of a feature space design and a supervised binary classification. In this report, we assume that the feature space has been already constructed, i.e., the steganalyst has a set of training features and needs to train a binary classifier. Any machine learning tool can be used for this task and its parameters can be tuned through cross-validation, a standard a...

The paper deals with a steganalysis of PQ algorithm by means of neural networks. The paper continues with the research of steganalysis by means of neural networks and brings results for the other steganography tool and also simulation results for different settings of neural network.

Retention of secrecy is one of the significant features during communication activity. Steganography is one of the popular methods to achieve secret communication between sender and receiver by hiding message in any form of cover media such as an audio, video, text, images etc. Least significant bit encoding is the simplest encoding method used by many steganography programs to hide secret mess...

There have been a number of steganography embedding techniques proposed over the past few years. In turn the development of these techniques have led to an increased interest in steganalysis techniques. More specifically Universal steganalysis techniques have become more attractive since they work independently of the embedding technique. In this work, our goal is to compare a number of univers...

Steganography is the field of hiding messages in apparently innocuous media (e.g. images), and steganalysis is the field of detecting these covert messages. Almost all steganalysis consists of hand-crafted tests or human visual inspection to detect whether a file contains a message hidden by a specific steganography algorithm. These approaches are very fragile – trivial changes in a steganograp...

Information fusion tries to determine the best set of experts in a given problem domain and devise an appropriate function that can optimally combine the decisions of the individual experts. Only few systematic approaches to information fusion exist so far in the signal processing field of steganalysis. Under the basic assumption that steganalysis can be seen as a statistical pattern recognitio...