In this paper, we propose a regression framework for steganalysis of digital images that utilizes the recently proposed rich models – high-dimensional statistical image descriptors that have been shown to substantially improve classical (binary) steganalysis. Our proposed system is based

The detection of LSB steganography is a question of common interest in the research of steganalysis techniques. In this paper, the distribution of the difference between the current pixel value and its neighborhood average pixel value is statistically modeled, and then the variance

covering a wide range of statistical dependencies. Due to its high dimensionality, we combine the rich model with ensemble classifiers and construct detectors for six modern JPEG domain steganographic schemes: nsF5, model-based steganography, YASS, and schemes that use side information at the embedder

This paper deals with the detection of hidden bits in the Least Significant Bit (LSB) plane of a natural colour image. The mean Level and the covariance matrix of the image, considered as a quantized Gaussian random matrix, are unknown. An adaptive statistical test is preferred such that its

counterpart of stganography is to detect the presence of it. In this paper, we present a new universal steganalysis method based on statistical models of the image’s discrete cosine transform (DCT) coefficients. In fact, the block-based DCT by proper reorganization of its coefficients can have similar

in steganalysis, however, have shown that more complex and higher-dimensional image models could deliver substantially better performance. In this dissertation, we propose a novel framework for steganalysis of digital images in which we replace SVMs with the ensemble classifier, a scalable machine

This paper presents a novel steganalysis technique to attack quantization index modulation (QIM) steganography. Our method is based on the observation that QIM embedding disturbs neighborhood correlation in the transform domain. We estimate the probability density function (pdf) of this statistical

Abstract—Most current steganographic schemes em-bed the secret payload by minimizing a heuristically defined distortion. Similarly, their security is evalu-ated empirically using classifiers equipped with rich image models. In this paper, we pursue an alternative approach based on a locally

* Project 60473022 supported by NSFC The detection of LSB steganography is a question of common interest in the research of steganalysis techniques. In this paper, the distribution of the difference between the current pixel value and its neighborhood average pixel value is statistically modeled

Abstract: Online learning initia-tives over the past decade have become increasingly comprehen-sive in their selection of courses and sophisticated in their presen-tation, culminating in the recent announcement of a number of consortium and startup activities that promise to make a university