One-class classification (OCC) algorithms aim to build classification models when the
negative class is either absent, poorly sampled or not well defined. This unique situation …

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 …

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

The success rate for blind or universal steganalysis lies in the ability to extract the statistical
footprints of image features. Further, the choice of machine learning (ML) algorithm is crucial …

The advent of Internet of Things (IoT) has escalated the information sharing among various
smart devices by many folds, irrespective of their geographical locations. Recently …

We consider the problem of detecting spatial domain least significant bit (LSB) matching
steganography in grayscale images, which has proved much harder than for its counterpart …

Blind steganalysis based on classifying feature vectors derived from images is becoming
increasingly more powerful. For steganalysis of JPEG images, features derived directly in …

The goal of this paper is to determine the steganographic capacity of JPEG images (the
largest payload that can be undetectably embedded) with respect to current best …

Computer graphics rendering software is capable of generating highly photorealistic images
that can be impossible to differentiate from photographic images. As a result, the unique …

Techniques for information hiding (steganography) are becoming increasingly more
sophisticated and widespread. With high-resolution digital images as carriers, detecting …