Steganography refers to information or file that has been concealed inside a digital media. Steganalysis is used to detect and/or estimate potentially hidden information from observed data with a little or no knowledge about the steganography algorithm and its parameters. Current trend in steganalysis seems to suggest two extreme approaches (a) little or no statistical assumption about the imag...

The purpose of image steganalysis is to detect the presence of hidden messages in cover images. Steganalysis can be considered as a pattern recognition process to decide which class a test image belongs to: the innocent photographic image or the stego-image. We compare harmony search algorithm and particle swarm optimization algorithm based feature selection for image steganalysis. Experiment r...

In the paper we extend an existing information fusion based audio steganalysis approach by three different kinds of evaluations: The first evaluation addresses the so far neglected evaluations on sensor level fusion. Our results show that this fusion removes content dependability while being capable of achieving similar classification rates (especially for the considered global features) if com...

This paper considers the research goal of dependable steganalysis: where false positives occur once in a million or less, and this rate is known with high precision. Despite its importance for real-world application, there has been almost no study of steganalysis which produces very low false positives. We test existing and novel classifiers for their low false-positive performance, using milli...

techniques attempt to differentiate between stego-objects and cover-objects. In recent work we developed an analytic upper bound for the steganographic capacity of LSB based steganographic techniques for a given false of detection. In this paper we look at adaptive steganographic techniques. Adaptive steganographic techniques steps to escape detection. We explore different techniques that can b...

In this tutorial, we introduce the basic theory behind Steganography and Steganalysis, and present some recent algorithms and developments of these fields. We show how the existing techniques used nowadays are related to Image Processing and Computer Vision, point out several trendy applications of Steganography and Steganalysis, and list a few great research opportunities just waiting to be ad...

In this paper, a general blind steganalysis system is proposed, in which the statistical moments of characteristic functions of the prediction-error image, the test image, and their wavelet subbands are selected as features. Artificial neural network is utilized as the classifier. The performance of the proposed steganalysis system is significantly superior to the prior arts.

Steganography is the art of hiding the very presence of communication by embedding secret messages into innocuous looking cover documents, such as digital images. Detection of steganography, estimation of message length, and its extraction belong to the field of steganalysis. Steganalysis has recently received a great deal of attention both from law enforcement and the media. In our paper, we c...

Deep learning and convolutional neural networks (CNN) have been intensively used in many image processing topics during last years. As far as steganalysis is concerned, the use of CNN allows reaching the state-of-the-art results. The performances of such networks often rely on the size of their learning database. An obvious preliminary assumption could be considering that the bigger a database ...

Ooverwhelming growth in communication technology and usage of internet has greatly facilitated transfer of data. The threat of unauthorized data access is increasing along with it. Since last decade, too many data hiding methods have been proposed. Because of large number of redundant bits, image has become popular carrier for data hiding techniques. The advancement in image steganography has p...