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 …

Steganalysis and steganography are the two different sides of the same coin.
Steganography tries to hide messages in plain sight while steganalysis tries to detect their …

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

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 …

Automatic cost learning for steganography based on deep neural networks is receiving
increasing attention. Steganographic methods under such a framework have been shown to …

Steganographic schemes are commonly designed in a way to preserve image statistics or
steganalytic features. Since most of the state-of-the-art steganalytic methods employ a …

Adaptive JPEG steganographic schemes are difficult to preserve the image texture features
in all scales and orientations when the embedding changes are constrained to the …

Adoption of deep learning in image steganalysis is still in its initial stage. In this paper, we
propose a generic hybrid deep-learning framework for JPEG steganalysis incorporating the …

This paper presents an empirical study on applying convolutional neural networks (CNNs) to
detecting J-UNIWARD--one of the most secure JPEG steganographic method. Experiments …

Posting images on social network platforms is happening everywhere and every single
second. Thus, the communication channels offered by various social networks have a great …