The detection of data hiding in RGB images using statistical steganalysis
العناوين الأخرى
كشف البيانات المخفية في صور RGB باستخدام تحليل غطاء الإخفاء الإحصائي
مقدم أطروحة جامعية
مشرف أطروحة جامعية
أعضاء اللجنة
Alya, Muhammad Ahmad
Kayid, Ahmad
الجامعة
جامعة الشرق الأوسط
الكلية
كلية تكنولوجيا المعلومات
القسم الأكاديمي
قسم علم الحاسوب
دولة الجامعة
الأردن
الدرجة العلمية
ماجستير
تاريخ الدرجة العلمية
2017
الملخص الإنجليزي
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
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الموضوعات
عدد الصفحات
77
قائمة المحتويات
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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
لغة النص
الإنجليزية
نوع البيانات
رسائل جامعية
رقم السجل
BIM-762708
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر