Over the past few years, neural networks have exhibited remarkable results for various
applications in machine learning and computer vision. Weight initialization is a significant …

Any computer vision application development starts off by acquiring images and data, then
preprocessing and pattern recognition steps to perform a task. When the acquired images …

Steganography detectors built as deep convolutional neural networks have firmly
established themselves as superior to the previous detection paradigm-classifiers based on …

Image steganalysis is a technique for detecting data hidden in images. Recent research has
shown the powerful capabilities of using convolutional neural networks (CNN) for image …

Digital steganography has a long history, starting to be developed in the 90s until now. The
main aspects of early steganography are security, imperceptibility, and payload. Security is …

For steganalysis, many studies showed that convolutional neural network (CNN) has better
performances than the two-part structure of traditional machine learning methods. Existing …

Recent studies have indicated that the architectures of convolutional neural networks
(CNNs) tailored for computer vision may not be best suited to image steganalysis. In this …

Image steganalysis is to discriminate innocent images and those suspected images with
hidden messages. This task is very challenging for modern adaptive steganography, since …

Generative adversarial network has shown to effectively generate artificial samples
indiscernible from their real counterparts with a united framework of two subnetworks …

Mammographic risk scoring has commonly been automated by extracting a set of
handcrafted features from mammograms, and relating the responses directly or indirectly to …