A natural image model approach to splicing detection
Proceedings of the 9th workshop on Multimedia & security
Detection of Copy-Move Forgery in Digital Images Using SIFT Algorithm
PACIIA '08 Proceedings of the 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application - Volume 02
Fusion Based Blind Image Steganalysis by Boosting Feature Selection
IWDW '07 Proceedings of the 6th International Workshop on Digital Watermarking
Effective image splicing detection based on image chroma
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Steganalysis by subtractive pixel adjacency matrix
IEEE Transactions on Information Forensics and Security
Detecting digital image splicing in chroma spaces
IWDW'10 Proceedings of the 9th international conference on Digital watermarking
Exposing digital forgeries in color filter array interpolated images
IEEE Transactions on Signal Processing - Part II
Exposing Digital Forgeries in Complex Lighting Environments
IEEE Transactions on Information Forensics and Security - Part 1
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Second order statistical features (e.g. Markov transposition probability matrix and gray level co-occurrence matrix) have been proved to be effective for passive image forgery detection in the past few years. In this paper, third order statistical features are proposed for image splicing detection. We model the thresholded adjacent difference block DCT coefficient array of an image as conditional co-occurrence probability matrix, second order Markov transition probability matrix and second order co-occurrence matrix. Since the dimensionality exponentially depends on the order, dimensionality of the third order features is much larger than that of second order features, principal component analysis (PCA) is therefore introduced in our work to overcome the high dimensionality introduced computational complexity and the possible overfitting for a kernel based supervised classifier. Experimental results show that conditional co-occurrence probability matrix outperforms second order features and PCA is proved to be an effective dimensionality reduction tool for image splicing detection. We also test the robustness of third order statistical features, despite higher dimensionality, third order statistical features demonstrate the same robustness as that of second order features.