(NMT) in 2002. He is a great mentor with broad and long view and continuous scientific passion. I truly appreciate his advice and guidance in my research and his generous support throughout my PhD study as well as his encouraging and affording me to attend the prestigious international academic conferences, and hence broaden my horizon and make me thriving. Without him, I could not have this op...

The steganalysis for JPEG image is an important research topic, as the enormous popularity of on Internet. However, stego noise feature extraction process existing deep learning-based steganalytic methods are not adaptive enough to content image, which may lead suboptimal performance. In order solve this issue, network, named SNENet, proposed. module network specifically designed steganalysis, ...

My research motivation came from a project supported by Naval Research Laboratory (NRL) where I was working on an algorithm to provide better stealthiness for hiding data inside JPEG images. As a result, with the guidance of my advisor, Dr. Newman, and Ira S. Moskowitz from Center for High Assurance Computer Systems, NRL, we developed J2 steganography algorithm which was based on hiding data in...

A standard way to design steganalysis features for digital images is to choose a pixel predictor, use it to compute a noise residual, and then form joint statistics of neighboring residual samples (co-occurrence matrices). This paper proposes a general data-driven approach to optimizing predictors for steganalysis. First, a local pixel predictor is parametrized and then its parameters are deter...

In this paper, an unsupervised steganalysis method that combines artificial training sets and supervised classification is proposed. We provide a formal framework for unsupervised classification of stego and cover images in the typical situation of targeted steganalysis (i.e., for a known algorithm and approximate embedding bit rate). We also present a complete set of experiments using 1) eight...

With most image steganalysis traditionally based on supervised machine learning methods, the size of training data has remained static at up to 20000 training examples. This potentially leads to the classifier being undertrained for larger feature sets and it may be too narrowly focused on characteristics of a source of cover images, resulting in degradation in performance when the testing sour...