A false negative study of the steganalysis tool: Stegdetect
- Published
- Accepted
- Subject Areas
- Cryptography
- Keywords
- Steganalysis, Stegdetect, Steganography, False Negative Rates, Data Embedding, Digital Forensics
- Copyright
- © 2018 Aziz et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. A false negative study of the steganalysis tool: Stegdetect. PeerJ Preprints 6:e27339v1 https://doi.org/10.7287/peerj.preprints.27339v1
Abstract
Steganography and Steganalysis in recent years have become an important area of research involving dierent applications. Steganography is the process of hiding secret data into any digital media without any signicant notable changes in a cover object, while steganalysis is the process of detecting hiding content in the cover object. In this study, we evaluated one of the modern automated steganalysis tools, Stegdetect, to study its false negative rates when analysing a bulk of images. In so doing, we used JPHide method to embed a randomly generated messages into 2000 JPEG images. The aim of this study is to help digital forensics analysts during their investigations by means of providing an idea of the false negative rates of Stegdetect. This study found that (1) the false negative rates depended largely on the tool's sensitivity values, (2) the tool had a high false negative rate between the sensitivity values from 0.1 to 3.4 and (3) the best sensitivity value for detection of JPHide method was 6.2. It is recommended that when analysing a huge bulk of images forensic analysts need to take into consideration sensitivity values to reduce the false negative rates of Stegdetect.
Author Comment
This is a submission to PeerJ Computer Science for review.
