Application of Steganography for Anonymity through the Internet

In this paper, a novel steganographic scheme based on chaotic iterations is proposed. This research work takes place into the information hiding security framework. The applications for anonymity and privacy through the Internet are regarded too. To guarantee such an anonymity, it should be possible to set up a secret communication channel into a web page, being both secure and robust. To achieve this goal, we propose an information hiding scheme being stego-secure, which is the highest level of security in a well defined and studied category of attacks called "watermark-only attack". This category of attacks is the best context to study steganography-based anonymity through the Internet. The steganalysis of our steganographic process is also studied in order to show it security in a real test framework.Comment: 14 page

JPEG steganography with particle swarm optimization accelerated by AVX

Digital steganography aims at hiding secret messages in digital data transmitted over insecure channels. The JPEG format is prevalent in digital communication, and images are often used as cover objects in digital steganography. Optimization methods can improve the properties of images with embedded secret but introduce additional computational complexity to their processing. AVX instructions available in modern CPUs are, in this work, used to accelerate data parallel operations that are part of image steganography with advanced optimizations.Web of Science328art. no. e544

Is ensemble classifier needed for steganalysis in high-dimensional feature spaces?

International audienceThe ensemble classifier, based on Fisher Linear Discriminant base learners, was introduced specifically for steganalysis of digital media, which currently uses high-dimensional feature spaces. Presently it is probably the most used method to design supervised classifier for steganalysis of digital images because of its good detection accuracy and small computational cost. It has been assumed by the community that the classifier implements a non-linear boundary through pooling binary decision of individual classifiers within the ensemble. This paper challenges this assumption by showing that linear classifier obtained by various regularizations of the FLD can perform equally well as the ensemble. Moreover it demonstrates that using state of the art solvers linear classifiers can be trained more efficiently and offer certain potential advantages over the original ensemble leading to much lower computational complexity than the ensemble classifier. All claims are supported experimentally on a wide spectrum of stego schemes operating in both the spatial and JPEG domains with a multitude of rich steganalysis feature sets