The detection of data hiding in RGB images using statistical steganalysis
Other Title(s)
كشف البيانات المخفية في صور RGB باستخدام تحليل غطاء الإخفاء الإحصائي
Dissertant
Thesis advisor
Comitee Members
Alya, Muhammad Ahmad
Kayid, Ahmad
University
Middle East University
Faculty
Faculty of Information Technology
Department
Computer Science Department
University Country
Jordan
Degree
Master
Degree Date
2017
English Abstract
Steganalysis, the science and technology of detecting the presence of hidden data inside digital media, is a counter measure against information hiding techniques that can be used for illegitimate purposes.
The work in this thesis presents a steganalysis model that uses statistical texture features and the machine learning approach to detect the presence of hidden data in RGB color images.
The work analyzes features of an RGB image as a composite unit, as well as analyzing individual color channels and dual combinations of the channels.
The feature set used in this study consists of 26 features per channel, which includes the Gray Level Co-Occurrence Matrix (GLCM) features of correlation, contrast, homogeneity and energy, calculated for full bytes, half-bytes, 3-bit and 2-bit fragments of individual channels, Entropy of full bytes and half bytes, skewness of full bytes and half bytes, and additional statistical features.
The features are applied to single channels, and the single channel features are merged into dual and three-channel image feature sets.
The main machine learning binary classifier that is selected for this work is the Support Vector Machine (SVM) algorithm.
The experimental work used two image datasets of 1500 BMP images each, for training and validation of the model, and an independent image dataset of 1000 uncompressed PNG images for testing purposes.
Stego image datasets were created from the clean images datasets, which were embedded with secret data using 2LSB and 4LSB steganography techniques.
The experimental results for the validation phase showed detection accuracy of 100% for the 4LSB RGB stego images, and 99.73% for the 2LSB RGB stego images.
Similar results were obtained, which shows the power of the SVM classifier in detecting pattern changes in stego images even when one channel is changed, individual channels (R, G, B) and dual channels (RG, RB, GB) were analyzed.
Also, when only one channel was embedded with data, which was the blue channel, the same results were obtained.
The testing phase analyzed 1000 PNG stego images, which confirmed results of the validation phase.
The Discriminant Analysis (DA) classifier was used for comparison with the SVM classifier, and the results showed that the SVM classifier gave higher detection accuracy.
MATLAB 2015a was used in the implementation of the image processing and classification parts of the proposed model
Main Subjects
Information Technology and Computer Science
Topics
No. of Pages
77
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Literature review.
Chapter Three : Methodology and the proposed model.
Chapter Four : Experimental results and discussion.
Chapter Five : Conclusion and future work.
References.
American Psychological Association (APA)
Rasul, Zayd Ibrahim Rasul. (2017). The detection of data hiding in RGB images using statistical steganalysis. (Master's theses Theses and Dissertations Master). Middle East University, Jordan
https://search.emarefa.net/detail/BIM-762708
Modern Language Association (MLA)
Rasul, Zayd Ibrahim Rasul. The detection of data hiding in RGB images using statistical steganalysis. (Master's theses Theses and Dissertations Master). Middle East University. (2017).
https://search.emarefa.net/detail/BIM-762708
American Medical Association (AMA)
Rasul, Zayd Ibrahim Rasul. (2017). The detection of data hiding in RGB images using statistical steganalysis. (Master's theses Theses and Dissertations Master). Middle East University, Jordan
https://search.emarefa.net/detail/BIM-762708
Language
English
Data Type
Arab Theses
Record ID
BIM-762708