Date of Award

Spring 2005

Document Type

Thesis

Degree Name

Master of Science in Electrical Engineering - (M.S.)

Department

Electrical and Computer Engineering

First Advisor

Yun Q. Shi

Second Advisor

Constantine N. Manikopoulos

Third Advisor

MengChu Zhou

Abstract

A novel reversible data embedding method was reported in a recent IEEE journal article. The method was based on difference expansion (DE) technique. It used redundancy in digital images to achieve a high embedding capacity, while keeping visual distortion of the stego-image low. In this thesis, this technique was studied and experimentally evaluated. An effective steganalysis scheme for this DE-based reversible data embedding method was proposed, which used 12-dimensional feature vectors and a Bayes Classifier. The proposed steganalysis scheme steadily achieved a correct classification rate of 99%.

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