Improving RLRN Image Splicing Detection with the Use of PCA and Kernel PCA

Scientific World Journal

Volumn 2014, Issue , 2014, Pages

  Moghaddasi, Zahra ^a   Jalab, Hamid A ^a   Md Noor, Rafidah ^a   Aghabozorgi, Saeed ^a  

^a UNIVERSITY OF MALAYA   (Malaysia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; COMPARATIVE STUDY; COVARIANCE; EXTRACTION; IMAGE PROCESSING; KERNEL PRINCIPAL COMPONENT ANALYSIS; MULTIVARIATE ANALYSIS; PATTERN RECOGNITION; PRINCIPAL COMPONENT ANALYSIS; RUN LENGTH RUN NUMBER ALGORITHM; SUPPORT VECTOR MACHINE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; HUMAN; PHOTOGRAPHY; PROCEDURES; SIGNAL PROCESSING; TRENDS;

HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; PHOTOGRAPHY; PRINCIPAL COMPONENT ANALYSIS; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 84931366666     PISSN: 23566140     EISSN: 1537744X     Source Type: Journal    
DOI: 10.1155/2014/606570     Document Type: Article

References (27)

1. A. Rocha, W. Scheirer, T. Boult, and S. Goldenstein, "Vision of the unseen: current trends and challenges in digital image and video forensics", ACM Computing Surveys, vol. 43, no. 4, article no. 26, 2011.
2. W. Wang, J. Dong, and T. Tan, "A survey of passive image tampering detection", in Digital Watermarking, pp. 308-322, Springer, 2009.
3. H. Farid, Detecting Digital Forgeries Using Bispectral Analysis, 1999.
4. T.-T. Ng, S.-F. Chang, and Q. Sun, "Blind detection of photomontage using higher order statistics", in Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS'04), pp. V-688-V-691, IEEE, May 2004.
5. D. Fu, Y. Q. Shi, and W. Su, "Detection of image splicing based on hilbert-huang transform and moments of characteristic functions with wavelet decomposition", in Digital Watermarking, pp. 177-187, Springer, New York, NY, USA, 2006.
6. X. Li, T. Jing, and X. Li, "Image splicing detection based on moment features and Hilbert-Huang Transform", in Proceedings of the IEEE International Conference on Information Theory and Information Security (ICITIS'10), pp. 1127-1130, December 2010.
7. Y. Q. Shi, C. Chen, and W. Chen, "A natural image model approach to splicing detection", in Proceedings of the 9th workshop on Multimedia & security, pp. 51-62, ACM, September 2007.
8. J. Zhang, Y. Zhao, and Y. Su, "Anew approachmerging Markov and DCT features for image splicing detection", in Proceedings of the IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS'09), pp. 390-394, November 2009.
9. Y. Q. Shi, C. Chen, and W. Chen, "A Markov process based approach to effective attacking JPEG steganography", in Information Hiding, Springer, 2007.
10. Z. He, W. Lu, W. Sun, and J. Huang, "Digital image splicing detection based on Markov features in DCT and DWT domain", Pattern Recognition, vol. 45, no. 12, pp. 4292-4299, 2012.
11. C. Chen and Y. Q. Shi, "JPEG image steganalysis utilizing both intrablock and interblock correlations", in Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS'08), pp. 3029-3032, Seattle, Wash, USA, May 2008.
12. T.-T. Ng, S.-F. Chang, and Q. Sun, "A data set of authentic and spliced image blocks", ADVENT Technical Report 203-2004, Columbia University, New York City, NY, USA, 2004, http://www.ee.columbia.edu/ln/dvmm/.
13. J. Dong, W. Wang, T. Tan, and Y. Q. Shi, "Run-length and edge statistics based approach for image splicing detection", in Digital Watermarking, pp. 76-87, Springer, 2009.
14. Z. He, W. Lu, and W. Sun, "Improved run length based detection of digital image splicing", in Digital Forensics and Watermarking, pp. 349-360, Springer, 2012.
15. X. Zhao, J. Li, S. Li, and S. Wang, "Detecting digital image splicing in chroma spaces", in Digital Watermarking, vol. 6526 of Lecture Notes in Computer Science, pp. 12-22, Springer, Berlin, Germany, 2011.
16. "CASIA tampered image detection evaluation database", http://forensics.idealtest.org/.
17. D. Zou, Y. Q. Shi, W. Su, and G. Xuan, "Steganalysis based on Markov model of thresholded prediction-error image", in Proceedings of the IEEE International Conference on Multimedia and Expo (ICME'06), pp. 1365-1368, IEEE, July 2006.
18. M. M. Galloway, "Texture analysis using gray level run lengths", Computer Graphics and Image Processing, vol. 4, no. 2, pp. 172-179, 1975.
19. X. Tang, "Texture information in run-length matrices", IEEE Transactions on Image Processing, vol. 7, no. 11, pp. 1602-1609, 1998.
20. K. Anusudha, S. A. Koshie, S. S. Ganesh, and K. Mohanaprasad, "Image splicing detection involving moment-based feature extraction and classification using artificial neural networks", ACEEE International Journal on Signal & Image Processing, vol. 1, no. 3, p. 9, 2010.
21. A. Ghodsi, Dimensionality Reduction a Short Tutorial, Department of Statistics and Actuarial Science, University of Waterloo, Ontario, Canada, 2006.
22. J. E. Jackson, A User's Guide to Principal Components, John Wiley & Sons, New York, NY, USA, 1991.
23. I. T. Jolliffe, Principal Component Analysis, vol. 487, Springer, New York, NY, USA, 1986.
24. B. Schölkopf, A. Smola, and K.-R. Müller, "Kernel principal component analysis", in Artificial Neural Networks-ICANN'97, vol. 1327 of Lecture Notes in Computer Science, pp. 583-588, Springer, Berlin, Germany, 1997.
25. I. K. Fodor, "A survey of dimension reduction techniques", Tech. Rep. UCRL-ID-148494, Lawrence Livermore National Laboratory, 2002.
26. L. J. E. O. Postma, H. J. van den Herik, and L. J. van der Maaten, "Dimensionality reduction: a comparative review", Journal of Machine Learning Research, vol. 10, pp. 66-71, 2009.
27. C.-C. Chang and C.-J. Lin, "LIBSVM: a Library for support vector machines", ACMTransactions on Intelligent Systems and Technology, vol. 2, no. 3, article 27, 2011.