A novel model for splicing detection

With the advent of digital technology, digital image has gradually taken the place of the original analog photograph, and the forgery of digital image has become more and more easy and indiscoverable. Image splicing is a commonly used technique in image tampering. To implement image splicing detection a blind, passive and effective splicing detection scheme was proposed in this paper. Image splicing detection can be treated as a two-class pattern recognition problem, the model was based on moment features and some image quality metrics (IQMs) extracted from the given test image, which are sensitive to spliced image. Artificial neural network (ANN) is chosen as a classifier to train and test the given images. This model can measure statistical differences between original image and spliced image. Experimental results demonstrate that this new splicing detection algorithm is effective and reliable; indicating that the proposed approach has a broad application prospect.

[1]  Wei Su,et al.  Detection of Image Splicing Based on Hilbert-Huang Transform and Moments of Characteristic Functions with Wavelet Decomposition , 2006, IWDW.

[2]  Chengyun Yang,et al.  Image steganalysis based on moments of characteristic functions using wavelet decomposition, prediction-error image, and neural network , 2005, 2005 IEEE International Conference on Multimedia and Expo.

[3]  Bülent Sankur,et al.  Statistical evaluation of image quality measures , 2002, J. Electronic Imaging.

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

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

[6]  Bülent Sankur,et al.  Statistical analysis of image quality measures , 2000, 2000 10th European Signal Processing Conference.

[7]  Yun Q. Shi,et al.  Steganalyzing Texture Images , 2007, 2007 IEEE International Conference on Image Processing.

[8]  Nasir D. Memon,et al.  Steganalysis using image quality metrics , 2003, IEEE Trans. Image Process..

[9]  S. Jaafar,et al.  Diabetes mellitus forecast using artificial neural network (ANN) , 2005, 2005 Asian Conference on Sensors and the International Conference on New Techniques in Pharmaceutical and Biomedical Research.

[10]  Hany Farid,et al.  Exposing digital forgeries by detecting inconsistencies in lighting , 2005, MM&Sec '05.

[11]  Shih-Fu Chang,et al.  Blind detection of photomontage using higher order statistics , 2004, 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512).