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Steganalysis of Digital Images Based on Optimization in Feature Space

Seyedhosseini Tarzjani, Mojtaba | 2009

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 39550 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Ghaemmaghami, Shahrokh
  7. Abstract:
  8. Nowadays, steganography is one of the secure communication methods. Exploitation of modern technologies has increased transmission bandwidth to a considerable scale. As a result of this, the multimedia signals such as Audio and Image have been used in communications widely. This application has led to the prevalent use of these signals as cover signals for carrying hidden messages. Given the proliferation of digital images, especially on the Internet, and given the large amount of redundant bits present in the digital representation of an image, images are the most popular cover objects for steganography. Simultaneously, steganalysis tries to defeat the very purpose of steganography by detecting the presence of hidden communication. Modern steganalysis techniques try to find some features of the cover signal that are sensitive to data embedding and would change detectably due to steganography. In this thesis, we introduce two steganalysis methods based on the features of gray level run length matrix. It is shown that these features can significantly be affected by the embedded message bits. The extracted features are examined by SVM and neural network classifiers that can distinguish between stego and clean images. These steganalysis techniques can be employed to detect LSB based steganography methods. Experimental results are given to demonstrate the competitively higher performance of the proposed methods, as compared to other well-known steganalysis methods, at different embedding rates. At last we apply two feature reduction techniques to the features vector to design an efficient method with less complexity.
  9. Keywords:
  10. Watermarking ; Neural Network ; Steganalysis ; Support Vector Machine (SVM) ; Feature Reduction

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