Joint image splicing detection in DCT and Contourlet transform domain

Journal of Visual Communication and Image Representation

Volumn 40, Issue Part B, 2016, Pages 449-458

  Zhang, Qingbo ^a   Lu, Wei ^a   Weng, Jian ^b  

^a SUN YAT SEN UNIVERSITY   (China)

^b JINAN UNIVERSITY   (China)

Author keywords

Contourlet transform; Discrete cosine transform; Ensemble classifier; Image splicing detection; Markov features

Indexed keywords

CLASSIFICATION (OF INFORMATION); COLOR; DISCRETE COSINE TRANSFORMS; IMAGE CODING; IMAGE ENHANCEMENT; MARKOV PROCESSES; SUPPORT VECTOR MACHINES;

BLOCK DISCRETE COSINE TRANSFORM; CONTOURLET TRANSFORM; DIGITAL IMAGE FORENSICS; ENSEMBLE CLASSIFIERS; IMAGE SPLICING DETECTION; MARKOV FEATURES; STATE-OF-THE-ART METHODS; SUPPORT VECTOR MACHINE (SVMS);

IMAGE CLASSIFICATION;

EID: 84992111311     PISSN: 10473203     EISSN: 10959076     Source Type: Journal    
DOI: 10.1016/j.jvcir.2016.07.013     Document Type: Article

References (35)

1. [1] Vyas, C., Lunagaria, M., A review on methods for image authentication and visual cryptography in digital image watermarking. IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Coimbatore, India, 2014, 1–6.
2. [2] Panchal, U.H., Srivastava, R., A comprehensive survey on digital image watermarking techniques. International Conference on Communication Systems and Network Technologies (CSNT), Gwalior, India, 2015, 591–595.
3. [3] Liu, M.C.S.W., Information forensics an overview of the first decade. IEEE Access 1 (2013), 167–200.
4. [4] Luo, W., Qu, Z., Pan, F., Huang, J., A survey of passive technology for digital image forensics. Front. Comput. Sci. China 1:2 (2007), 166–179.
5. [5] Elwin, J.G.R., Aditya, T.S., Shankar, S.M., Survey on passive methods of image tampering detection. International Conference on Communication and Computational Intelligence (INCOCCI), Erode, India, vol. 7, 2010, 431–436.
6. [6] Birajdar, G.K., Mankar, V.H., Digital image forgery detection using passive techniques: a survey. Digital Invest. 10:3 (2013), 226–245.
7. [7] He, Z., Lu, W., Sun, W., Huang, J., Digital image splicing detection based on Markov features in DCT and DWT domain. Pattern Recogn. 45:12 (2012), 4292–4299.
8. [8] Shi, Y.Q., Chen, C., Chen, W., A natural image model approach to splicing detection. The 9th ACM Workshop on Multimedia and Security, 2007, ACM, Dallas, Texas, USA, 51–62.
9. [9] Ng, T.-T., Chang, S.-F., A data set of authentic and spliced image blocks, Tech. Rep. 203-2004-3. 2004, Columbia University.
10. [10] Zhao, X., Wang, S., Li, S., Li, J., Passive image-splicing detection by a 2-D noncausal Markov model. IEEE Trans. Circ. Syst. Video Technol. 25:2 (2015), 185–199.
11. [11] Yan, C., Zhang, Y., Xu, J., Dai, F., Zhang, J., Dai, Q., Wu, F., Efficient parallel framework for HEVC motion estimation on many-core processors. IEEE Trans. Circ. Syst. Video Technol. 24:12 (2014), 2077–2089.
12. [12] Yan, C., Zhang, Y., Xu, J., Dai, F., Li, L., Dai, Q., Wu, F., A highly parallel framework for HEVC coding unit partitioning tree decision on many-core processors. IEEE Signal Process. Lett. 21:5 (2014), 573–576.
13. [13] Yan, C., Zhang, Y., Dai, F., Li, L., Highly parallel framework for HEVC motion estimation on many-core platform. Data Compression Conf. (DCC) 6:3 (2013), 63–72.
14. [14] Yan, C., Zhang, Y., Dai, F., Wang, X., Li, L., Dai, Q., Parallel deblocking filter for HEVC on many-core processor. Electron. Lett. 50:5 (2014), 367–368.
15. [15] Dong, J., Wang, W., Tan, T., Shi, Y.Q., Run-length and edge statistics based approach for image splicing detection. International Workshop on Digital Watermarking (IWDW), Berlin, Heidelberg, 2009, 76–87.
16. [16] He, Z., Sun, W., Lu, W., Lu, H., Digital image splicing detection based on approximate run length. Pattern Recogn. Lett. 32:12 (2011), 1591–1597.
17. [17] Cozzolino, D., Gragnaniello, D., Verdoliva, L., Image forgery localization through the fusion of camera-based, feature-based and pixel-based techniques. IEEE International Conference on Image Processing (ICIP), Paris, France, 2014, 5302–5306.
18. [18] Qiu, X., Li, H., Luo, W., Huang, J., A universal image forensic strategy based on steganalytic model. Proceedings of the 2nd ACM Workshop on Information Hiding and Multimedia Security, New York, USA, 2014, 165–170.
19. [19] Fridrich, J., Kodovský, J., Rich models for steganalysis of digital images. IEEE Trans. Inform. Forensics Security 7:3 (2012), 868–882.
20. [20] Bahrami, K., Kot, A.C., Fan, J., Splicing detection in out-of-focus blurred images. IEEE International Workshop on Information Forensics and Security (WIFS), Guangzhou, China, 2013, 144–149.
21. [21] Rao, M.P., Rajagopalan, A.N., Seetharaman, G., Harnessing motion blur to unveil splicing. IEEE Trans. Inform. Forensics Security 9:4 (2014), 583–595.
22. [22] Bahrami, K., Kot, A.C., Li, L., Li, H., Blurred image splicing localization by exposing blur type inconsistency. IEEE Trans. Inform. Forensics Security 10:5 (2015), 999–1009.
23. [23] Do, M.N., Vetterl, M., Image tampering detection by exposing blur type inconsistency. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy, 2014, 2654–2658.
24. [24] Do, M.N., Vetterl, M., Pyramidal directional filter banks and curvelets. IEEE International Conference on Image Processing (ICIP), Thessaloniki, Greece, vol. 3, 2001, 158–161.
25. [25] Do, M.N., Vetterli, M., The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans. Image Process. 14:12 (2005), 2091–2106.
26. [26] Po, D.D.-Y., Do, M.N., Directional multiscale modeling of images using the contourlet transform. IEEE Trans. Image Process. 15:6 (2006), 1610–1620.
27. [27] Guyon, I., Weston, J., Barnhill, S., Vapnik, V., Gene selection for cancer classification using support vector machines. Machine Learn. 46 (2002), 389–422.
28. [28] Wang, W., Dong, J., Tan, T., Effective image splicing detection based on image chroma. IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, 2009, 1257–1260.
29. [29] Kodovský, J., Fridrich, J., Holub, V., Ensemble classifiers for steganalysis of digital media. IEEE Trans. Inform. Forensics Security 7:2 (2012), 432–444.
30. [30] Kodovský, J., Fridrich, J., Steganalysis in high dimensions: fusing classifiers built on random subspaces. Proceedings of SPIE: Media Watermarking, Security, and Forensics III, California, USA, vol. 7880, 2011, 181–197.
31. [31] Verdoliva, L., Cozzolino, D., Poggi, G., A feature-based approach for image tampering detection and localization. IEEE International Workshop on Information Forensics and Security (WIFS), Atlanta, USA, 2014, 149–154.
32. [32] C.-C. Chang, C.-J. Lin, LIBSVM – a library for support vector machines, 2010. < http://www.csie.ntu.edu.tw/cjlin/libsvm>.
33. [33] Lu, W., Sun, W., Chung, F.-L., Lu, H., Revealing digital fakery using multiresolution decomposition and higher order statistics. Eng. Appl. Artif. Intell. 24:4 (2011), 666–672.
34. [34] Pevný, T., Bas, P., Fridrich, J., Steganalysis by subtractive pixel adjacency matrix. IEEE Trans. Inform. Forensics Security 5:2 (2010), 215–224.
35. [35] Shi, Y.Q., Chen, C., Xuan, G., Su, W., Steganalysis versus splicing detection. International Workshop on Digital Watermarking (IWDW), Busan, Korea, 2008, 158–172.