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BLOCK-BASED IMAGE STEGANALYSIS:
ALGORITHM AND PERFORMANCE EVALUATION
by
SeongHo Cho
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Ful llment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ELECTRICAL ENGINEERING)
August 2012
Copyright 2012 SeongHo Cho
Object Description
| Title | Block-based image steganalysis: algorithm and performance evaluation |
| Author | Cho, Seong Ho |
| Author email | seonghoc@usc.edu;surfsky@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Electrical Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2012-05-16 |
| Date submitted | 2012-08-01 |
| Date approved | 2012-08-01 |
| Restricted until | 2012-08-01 |
| Date published | 2012-08-01 |
| Advisor (committee chair) | Kuo, C.-C. Jay |
| Advisor (committee member) |
Ortega, Antonio K. Narayanan, Shrikanth S. Jenkins, Keith B. Huang, Ming-Deh Liu, Yan |
| Abstract | Traditional image steganalysis techniques are conducted with respect to the entire image. In this work, we aim to differentiate a stego image from its cover image based on steganalysis results of decomposed image blocks. We also target at the design of a multi-classifier which classifies stego images depending on their steganographic algorithms. As a natural image often consists of heterogeneous regions, its decomposition will lead to smaller image blocks, each of which is more homogeneous. We classify these image blocks into multiple classes and find a classifier for each class to decide whether a block is from a cover or stego image. Consequently, the steganalysis of the whole image can be conducted by fusing steganalysis results of all image blocks through a decision fusion process. Experimental results will be given to show the advantage of the proposed block-based image steganalysis for both binary classifier and multi-classifier. ❧ In addition, performance study on block-based image steganalysis in terms of block sizes and block numbers will be given in this work. First, we analyze the dependence of the steganalysis performance on one of these two factors, and show that a larger block size and a larger block number will lead to better steganalysis performance. Our study is verified by experimental results. For a given test image, there exists a trade-off between the block size and the block number. To exploit both effectively, we propose to use overlapping blocks to improve the steganalysis performance furthermore. Moreover, additional performance improvement of block-based image steganalysis with different number of classes and different classifiers will be shown with experimental results. ❧ Decision fusion for block-based image steganalysis will be discussed. As multiple block decisions are obtained from each image, decision fusion will play a crucial role in combining all the block decisions together to make a final decision for a given image. Among decision fusion techniques at different levels with different topologies, decision level fusion with parallel topology will be used for block-based image steganalysis. In addition, the importance of block decision result will be considered for decision fusion to improve the steganalysis performance. Experimental results with different decision level fusion techniques for block-based image steganalysis will be presented. ❧ Content-dependent feature selection for block-based image steganalysis will be proposed to reduce computational complexity with a significantly smaller number of features. Depending on the characteristic of block type, features with high discriminatory power will be selected for each block type. Several approaches to measure feature discriminatory power will be introduced. Finally, experimental result, which shows performance improvement using content-dependent feature selection, will be presented. |
| Keyword | steganalysis; steganography; decision fusion; feature selection |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m |
| Rights | Cho, Seong Ho |
| Access conditions | The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@usc.edu |
| Archival file | uscthesesreloadpub_Volume4/etd-ChoSeongHo-1103.pdf |
Description
| Title | Page 1 |
| Full text | BLOCK-BASED IMAGE STEGANALYSIS: ALGORITHM AND PERFORMANCE EVALUATION by SeongHo Cho A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Ful llment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (ELECTRICAL ENGINEERING) August 2012 Copyright 2012 SeongHo Cho |
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