Steganography is a science of hiding messages into multimedia documents. A message can be hidden in a document only if the content of a document has high redundancy. Although the embedded message changes the characteristics and nature of the document, it is required that these changes are difficult to be identified by an unsuspecting user. On the other hand, steganalysis develops theories, meth...

In the last few years, we have seen many new and powerful steganography and steganalysis techniques reported in the literature. In the following tutorial we go over some general concepts and ideas that apply to steganography and steganalysis. We review and discuss the notions of steganographic security and capacity. Some of the more recent image steganography and steganalysis techniques are ana...

To improve the detection accuracy and generalization of steganalysis, this paper proposes Steganalysis Contrastive Framework (SCF) based on contrastive learning. The SCF improves feature representation steganalysis by maximizing distance between features samples different categories minimizing same category. decrease computing complexity loss in supervised learning, we design a novel Loss (Steg...

Steganalysis is the process of cracking to identifying steganography by scrutinizing different Statistical parameter of a stego media. First of all a mathematical analysis may disclose statistical discrepancy in the stego medium. After that steganalysis process determines whether that media contains hidden message or not and then try to recover the message from it. The information hiding proces...

Steganalysis has many challenges; which include the accurate and efficient detection of hidden content within digital images. This paper focuses on the development of a new multi pixel comparison method used for the detection of steganographic content within digital images transmitted over mobile channels. The sensitivity of detecting hidden information within a digital image can be increased o...

Today, ensemble learning algorithms are proposed to address the challenges of high dimensional classification for steganalysis caused by the curse of dimensionality and obtain superior performance. In this paper, we propose a classifier ensemble algorithm based on improved Random Subspace Method (RSM) for high-dimensional blind steganalysis. Firstly, sequential forward selection (SFS) algorithm...

Feature Selection is a preprocessing technique with great significance in data mining applications that aims at reducing computational complexity and increase predictive capability of a learning system. This paper presents a new hybrid feature selection algorithm based on Discrete Firefly optimization technique with dynamic alpha and gamma parameters and t-test filter technique to improve detec...

Digital steganography is the art of hiding information in multimedia content, such that it remains perceptually and statistically unchanged. The detection of such covert communication is referred to as steganalysis. To date, steganalysis research has focused primarily on either, the extraction of features from a document that are sensitive to the embedding, or the inference of some statistical ...