The main idea of this paper is to compare feature selection methods for dimension reduction of the original dataset to reach optimization of steganalysis process by artificial neural networks (ANN). Feature selection methods are tools based on statistic exploited in pre-processing step of data mining workflow. These methods are very useful in a dimension reduction, removing of insignificant dat...

In this paper, we present a scheme based on feature mining and pattern classification to detect LSB matching steganography in grayscale images, which is a very challenging problem in steganalysis. Five types of features are proposed. In comparison with other well-known feature sets, the set of proposed features performs the best. We compare different learning classifiers and deal with the issue...

YASS is a steganographic algorithm for digital images that hides messages robustly in a key-dependent transform domain so that the stego image can be subsequently compressed and distributed as JPEG. Given the fact that state-of-the-art blind steganalysis methods of 2007, when YASS was proposed, were unable to reliably detect YASS, in this paper we steganalyze YASS using several recently propose...

Today, email management is not only a filing and storage challenge. Because law firms and attorneys must be equipped to take control of litigation, email authenticity must be unquestionable with strong chains of custody, constant availability, and tamper-proof security. Information Security and integrity are becoming more important as we use email for personal communication and business. Email ...

We investigate the performance of state of the art universal steganalyzers proposed in the literature. These universal steganalyzers are tested against a number of well-known steganographic embedding techniques that operate in both the spatial and transform domains. Our experiments are performed using a large data set of JPEG images obtained by randomly crawling a set of publicly available webs...

It is observed that the co-occurrence matrix, one kind of textural features proposed by Haralick et al., has played a very critical role in steganalysis. On the other hand, the data hidden in the image texture area has been known difficult to detect for years, and the modern steganographic schemes tend to embed data into complicated texture area where the statistical modeling becomes difficult....

Steganography refers to the technique of hiding secret messages into media such as text, audio, image and video without any suspicion, while steganalysis is the art and science of detection of the presence of steganography. Steganography can be used for the benefit of the mankind to serve us as well as by terrorists and criminals for malicious purposes. Both steganography and steganalysis have ...

HUGO is the content-based adaptive steganography method for spatial images which can approximately preserve the joint statistic of differences between up to four neighboring pixels in four different directions. But the steganalysis method based on the higher order local model can fail HUGO. In view of the above problem, an improved steganography is proposed. Firstly, by analyzing the higher ord...

This paper proposes to determine a sufficient number of images for reliable classification and to use feature selection to select most relevant features for achieving reliable steganalysis. First dimensionality issues in the context of classification are outlined, and the impact of the different parameters of a steganalysis scheme (the number of samples, the number of features, the steganograph...

Traditional image steganography modifies the content of the image more or less, it is hard to resist the detection of image steganalysis tools. To address this problem, a novel method named generative coverless information hiding method based on generative adversarial networks is proposed in this paper. The main idea of the method is that the class label of generative adversarial networks is re...