StegTrack
Veenu Bhasin, Punam Bedi, Aakarshi Goel, Sukanya Gupta · 2015
This paper presents the design and implementation of StegTrack, a novel proactive steganalysis tool. StegTrack is an antivirus like tool to track steganograms among images on a computer. To the best of our knowledge such a tool does not exist in literature. Once installed on a machine, StegTrack always remains active. It keeps track of user's entire file system and detects arrival of new images in the system. Every image entering the system is tested for steganography. Steganalysis, the process to detect the presence of the hidden data/message, has two major components feature extraction and classification. The StegTrack tool gives user the flexibility of choosing the feature extractor as well as classifier although a default is provided for both. The tool provides various feature extraction options like features based on Markov Model, co-occurrence matrix, neighboring joint density probabilities, Run-length matrix, SPAM and statistical features. The classifier opted as default in the StegTrack is ELM to provide multi-class classification results in real time. This proposed tool also provides a new feature - cleaning of stego-image, where image is rendered unfit for extracting hidden material from it. A prototype for the tool was implemented in MATLAB and Java.