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Research on Multi-view Features of Image Steganalysis Technique

Author: ZhengZiZuo
Tutor: ZhaoYao
School: Beijing Jiaotong University
Course: Signal and Information Processing
Keywords: Information Hiding Steganography Steganalysis Feature extraction DCT DWT DFT Airspace Predicted image ONPP dimensionality reduction Support Vector Machine
CLC: TP391.41
Type: Master's thesis
Year: 2010
Downloads: 88
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Abstract


With the rapid development of computer technology and network multimedia technology , information security is more and more attention. Steganography technology as a new branch in the field of information security research , the main the covert channel carrier information for effective detection, has injected new vitality into the development of information security . Especially the United States since the terrorist attacks of September 11, 2001 , steganography analysis more attention to national defense and military security has played an extremely important role . Steganography technology developing very rapidly in recent years , as the information detector provides a more mature program . Therefore, steganography has a high value of academic research and extensive application value . This paper presents a comprehensive feature extraction algorithm, and the use of advanced dimensionality reduction processing high-dimensional data to improve the classification performance . The main results include: (1) before and after the study of image steganography change in the characteristic coefficient of the DCT domain , DWT domain , the DFT domain and airspace for DCT domain , DWT domain , the DFT domain and airspace coefficient were proposed for a class of image feature extraction algorithm. While introducing the image predictive techniques , the extracted characteristic coefficients in the prediction image , correcting the original image feature , to reduce the difference of the vector of the image itself on the characteristics of the impact , and to improve the classification performance . (2) proposed based on ONPP stochastic subspace integrated strategy . Reduced feature dimension , avoiding the high - dimensional data caused by the curse of dimensionality . The manifold structure between Sample ONPP configured and maintain global geometric characteristics of the sample set . This dimensionality reduction programs that maintain the high performance of the system , but also reduce the complexity of the algorithm and the training time . Good classification results . (3) classifier design . Used in the classification process by analyzing the performance and the effectiveness of the feature vector of the classifier , support vector machine classifier , and nearest neighbor classifier results were compared and analyzed experiment has achieved very good results .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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