A Distributed Scheme for Image Splicing Detection

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.

[1]  Shih-Fu Chang,et al.  Detecting Image Splicing using Geometry Invariants and Camera Characteristics Consistency , 2006, 2006 IEEE International Conference on Multimedia and Expo.

[2]  Tomás Pevný,et al.  Steganalysis by subtractive pixel adjacency matrix , 2010, IEEE Trans. Inf. Forensics Secur..

[3]  Yun Q. Shi,et al.  A natural image model approach to splicing detection , 2007, MM&Sec.

[4]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[5]  Jessica J. Fridrich,et al.  On detection of median filtering in digital images , 2010, Electronic Imaging.

[6]  Dan Schonfeld,et al.  Video Event Classification and Image Segmentation Based on Noncausal Multidimensional Hidden Markov Models , 2009, IEEE Transactions on Image Processing.

[7]  Shih-Fu Chang,et al.  A Data Set of Authentic and Spliced Image Blocks , 2004 .

[8]  N. Balram,et al.  Noncausal predictive image codec , 1996, IEEE Trans. Image Process..

[9]  Jing Dong,et al.  Effective image splicing detection based on image chroma , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[10]  Tieniu Tan,et al.  Image tampering detection based on stationary distribution of Markov chain , 2010, 2010 IEEE International Conference on Image Processing.

[11]  Wei Lu,et al.  Digital image splicing detection based on Markov features in DCT and DWT domain , 2012, Pattern Recognit..

[12]  Jing Dong,et al.  Run-Length and Edge Statistics Based Approach for Image Splicing Detection , 2009, IWDW.

[13]  Siwei Lyu,et al.  Higher-order Wavelet Statistics and their Application to Digital Forensics , 2003, 2003 Conference on Computer Vision and Pattern Recognition Workshop.