Topological Data Analysis as Image Steganalysis Technique

Rashid, Rasber D. and Assad, A. and Jassim, Sabah A. (2018) Topological Data Analysis as Image Steganalysis Technique. In: Proc. SPIE 10668, Mobile Multimedia/Image Processing, Security, and Applications 2018, 106680J. SPIE.

[img] Text
2TDA as StegAnalysis tool_SPIE2018_Florida.pdf
Restricted to Registered users only

Download (935kB) | Request a copy
Official URL: https://www.spiedigitallibrary.org/conference-proc...

Abstract

Image Steganography is the technique of hiding sensitive data (secrete message) inside cover images in a way that no suspicion occurs to attackers, while steganalysis is the technique of detecting the embedded data by unauthorized persons. As a first step of detecting hidden data, distinguishing between original (Images without secrete message) and Stego (Images contain secrete message) is important. In this paper we design and propose a novel scheme based on the emerging field of Topological Data Analysis (TDA) concept of persistent homological (PH) invariants (e.g. No. of connected components), associated with certain image features. Selected group of Uniform Local Binary Pattern (LBP), which is a texture descriptor, codes representing the image features used to construct a sequence of simplicial complexes (SC) from an increasing sequence of distance thresholds (T). We calculate the corresponding non-increasing sequence of homological invariants which shows the speed at which the constructed sequence of SCs terminates. This approach is sensitive to differentiate original images from stego images. We test this approach on two different embedding techniques which are Traditional Least Significant Bits (TLSB) embedding technique, spatial Universal Wavelet Relative Distortion (S-UNIWARD) and LSB-Witness embedding technique together with a large number of images chosen randomly from large database of images. Preliminary results show that the PH sequence defines a discriminates criterion for steganalysis purpose with over 90% classification accuracy.

Item Type: Book Section
Additional Information: Presented at SPIE Commercial and Scientific Sensing and Imaging 2018, Orlando, Florida, United States
Uncontrolled Keywords: Steganalysis, Local Binary Pattern, Topological Data Analysis
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Science > School of Computing
Depositing User: Rachel Pollard
Date Deposited: 20 Aug 2019 14:18
Last Modified: 20 Aug 2019 14:18
URI: http://bear.buckingham.ac.uk/id/eprint/401

Actions (login required)

View Item View Item