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Analysis of Sensitivity of Features to Data Embedding in Blind Image Steganalysis

Heidari, Mortaza | 2013

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 44618 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Ghaemmaghami, Shahrokh
  7. Abstract:
  8. Steganalysis is the science of detecting covert communication. It is called blind (universal) if designed to detect stego images steganographied by a wide range of embedding methods. In this method, statistical properties of the image are explored, regardless the embedding procedure employed. The main problem for image steganalysis is to find sensitive features and characteristics of the image which make a statistically significant difference between the clean and stego images. In this thesis we propose a blind image steganalysis method based on the singular value decomposition (SVD) of the discrete cosine transform (DCT) coefficients that are revisited in this work in order to enhance the accuracy rate of the system for the low rate of insertion. We compute geometric mean, mean of log values, and statistical moments (mean, variance and skewness) of the SVDs of the DCT sub-blocks that are averaged over the whole image to construct a 480-element feature vector for steganalysis. Mathematical arguments as well as experimental results show the significant improvement achieved using the proposed scheme, as compared to some well-known blind image steganalysis methods.Another scheme is also developed in this research based on combining the presented feature set with the 216-D feature set of Liu known as a sensitive DCT feature set. Based on the given analysis and simulations,it is shown that the performance of the proposed method is superior to that of current methods for resistant JPG steganography like NSF5 and PQ, especially at low embedding rates
  9. Keywords:
  10. Steganalysis ; Steganography ; Singular Value Decomposition (SVD) ; Discrete Cosine Transform

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