Abstract
In order to capture more splicing traces and to improve the robustness to anti-forensics, combining different kinds of features are adopted for image detection work in recently years. However, the combined features inevitably increase the feature dimensionality and the computational complexity. In this paper, we propose a distributed approach to reducing the computational complexity introduced by the high-dimensional features in image splicing detection. We introduce first-order noncausal model to the splicing detection work and give the distributed solution to this model. The noncausal model is split into several small tasks which are solved simultaneously by the distributed scheme. Experimental results over the public Columbia Image Splicing Detection Evaluation Dataset show that the distributed noncausal model could differentiate between splicing images and natural ones effectively.
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References
Photo tampering throughout history, http://www.cs.dartmouth.edu/farid/research/digitaltampering
Dong, J., Wang, W., Tan, T., Shi, Y.Q.: Run-length and edge statistics based approach for image splicing detection. In: Kim, H.-J., Katzenbeisser, S., Ho, A.T.S. (eds.) IWDW 2008. LNCS, vol. 5450, pp. 76–87. Springer, Heidelberg (2009)
Hsu, Y.-F., Chang, S.-F.: Detecting image splicing using geometry invariants and camera characteristics consistency. In: Proceedings of International Conference on Multimedia and Expo, pp. 549–552 (2006)
Shi, Y.Q., Chen, C., Chen, W.: A natural image model approach to splicing detection. In: ACM Proceedings of the 9th Workshop on Multimedia and Security, pp. 51–62 (2007)
Wang, W., Dong, J., Tan, T.: Effective image splicing detection based on image chroma. In: Proceedings of IEEE International Conference on Image Processing (ICIP09), pp. 1257–1260 (2009)
Wang, W., Dong, J., Tan, T.: Image tampering detection based on stationary distribution of Markov chain. In: Proceedings of IEEE International Conference on Image Processing (ICIP10), pp. 2101–2104 (2010)
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), 4292–4299 (2012)
Pevny, T., Bas, P., Fridrich, J.: Steganalysis by subtractive pixel adjacency matrix. IEEE Trans. Inf. Forensics Secur. 5(2), 215–224 (2010)
Kirchner, M., Fridrich, J.: On detection of median filtering in digital images. In: SPIE Proceedings of Media Forensics and Security II, pp. 754110-1–754110-12 (2010)
Balram, N., Moura, J.M.F.: Noncausal predictive image codec. IEEE Trans. Image Process. 5(8), 1229–1242 (1996)
Ma, X., Schonfeld, S., Khokhar, A.A.: Video event classification and image segmentation based on noncausal multidimensional hidden Markov models. IEEE Trans. Image Process. 18(6), 1304–1313 (2009)
Farid, H., Lyu, S.: Higher-order wavelet statistics and their application to digital forensics. In: Proceedings of Computer Vision and Pattern Recognition Workshop (CVPR03), pp. 94–101 (2003)
Ng, T.T., Chang, S.F., Sun, Q.: A data set of authentic and spliced image blocks. Technical report, DVMM, Columbia University (2004). http://www.ee.columbia.edu/ln/dvmm/downloads/AuthSplicedDataSet/photographers.htm
DVMM Laboratory of Columbia University: Columbia Image Splicing Detection Evaluation Dataset. http://www.ee.columbia.edu/ln/dvmm/downloads/AuthSplicedDataSet/photographers.htm
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001). http://www.csie.ntu.edu.tw/cjlin/libsvm
Acknowledgements
This research work is funded by the National Science Foundation of China (61271316,61071152), 973 Programs (2010CB731403, 2010CB731406) of China and National “Twelfth Five-Year” Plan for Science & Technology Support (2012BAH38B04). Credits for the use of the Columbia Image Splicing Detection Evaluation Dataset are given to the DVMM Laboratory of Columbia University, CalPhotos Digital Library and the photographers listed in http://www.ee.columbia.edu/ln/dvmm/downloads/AuthSplicedDataSet/photographers.htm.
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Zhao, X., Wang, S., Li, S., Li, J., Lin, X. (2014). A Distributed Scheme for Image Splicing Detection. In: Shi, Y., Kim, HJ., Pérez-González, F. (eds) Digital-Forensics and Watermarking. IWDW 2013. Lecture Notes in Computer Science(), vol 8389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43886-2_23
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DOI: https://doi.org/10.1007/978-3-662-43886-2_23
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