Universal Image Steganalytic Method

In the paper we introduce a new universal steganalytic method in JPEG file format that is detecting well-known and also newly developed steganographic methods. The steganalytic model is trained by MHF-DZ steganographic algorithm previously designed by the same authors. The calibration technique with the Feature Based Steganalysis (FBS) was employed in order to identify statistical changes caused by embedding a secret data into original image. The steganalyzer concept utilizes Support Vector Machine (SVM) classification for training a model that is later used by the same steganalyzer in order to identify between a clean (cover) and steganographic image. The aim of the paper was to analyze the variety in accuracy of detection results (ACR) while detecting testing steganographic algorithms as F5, Outguess, Model Based Steganography without deblocking, JP Hide&Seek which represent the generally used steganographic tools. The comparison of four feature vectors with different lengths FBS (22), FBS (66) FBS(274) and FBS(285) shows promising results of proposed universal steganalytic method comparing to binary methods

A study on the false positive rate of Stegdetect

In this paper we analyse Stegdetect, one of the well-known image steganalysis tools, to study its false positive rate. In doing so, we process more than 40,000 images randomly downloaded from the Internet using Google images, together with 25,000 images from the ASIRRA (Animal Species Image Recognition for Restricting Access) public corpus. The aim of this study is to help digital forensic analysts, aiming to study a large number of image files during an investigation, to better understand the capabilities and the limitations of steganalysis tools like Stegdetect. The results obtained show that the rate of false positives generated by Stegdetect depends highly on the chosen sensitivity value, and it is generally quite high. This should support the forensic expert to have better interpretation in their results, and taking the false positive rates into consideration. Additionally, we have provided a detailed statistical analysis for the obtained results to study the difference in detection between selected groups, close groups and different groups of images. This method can be applied to any steganalysis tool, which gives the analyst a better understanding of the detection results, especially when he has no prior information about the false positive rate of the tool