Exposing digital forgeries by detecting inconsistencies in lighting
MM&Sec '05 Proceedings of the 7th workshop on Multimedia and security
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
Using noise inconsistencies for blind image forensics
Image and Vision Computing
Run-Length and Edge Statistics Based Approach for Image Splicing Detection
Digital Watermarking
Effective image splicing detection based on image chroma
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Exposing digital forgeries in color filter array interpolated images
IEEE Transactions on Signal Processing - Part II
Texture information in run-length matrices
IEEE Transactions on Image Processing
A comprehensive study on third order statistical features for image splicing detection
IWDW'11 Proceedings of the 10th international conference on Digital-Forensics and Watermarking
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Detecting splicing traces in the tampering color space is usually a tough work. However, it is found that image splicing which is difficult to be detected in one color space is probably much easier to be detected in another one. In this paper, an efficient approach for passive color image splicing detection is proposed. Chroma spaces are introduced in our work compared with commonly used RGB and luminance spaces. Four gray level run-length run-number (RLRN) vectors with different directions extracted from de-correlated chroma channels are employed as distinguishing features for image splicing detection. Support vector machine (SVM) is used as a classifier to demonstrate the performance of the proposed feature extraction method. Experimental results have shown that that RLRN features extracted from chroma channels provide much better performance than that extracted from R, G, B and luminance channels.