Many passive image tamper detection techniques have been presented in the expanding field of image forensics. Some of these techniques use a classifier for a final decision based on whole image statistics, resulting in a lack of forgery localization. The aim of this paper is to add localization to a previously published algorithm that uses grey-level co-occurrence matrix (GLCM) for extracting texture features from the chromatic component of an image (Cb or Cr component). Experimental results show that we can localize tampering for different sized regions with reasonable accuracy. The main trade-off is a diminishing detection accuracy as the region size decreases.

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Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 08/25/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspxAbstract. Passive forensics is increasing in significance due to the availability of various software tools that can be used to alter original content without visible traces and the increasing public awareness of such tampering. Many passive image tamper detection techniques have been proposed in the literature, some of which use feature extraction methods for tamper detection and localization. This work proposes a flexible methodology for detecting cloning in images based on the use of feature detectors. We determine whether a particular match is the result of a cloning event by clustering the matches using k-means clustering and using a support vector machine to classify the clusters. This descriptor-agnostic approach allows us to combine the results of multiple feature descriptors, increasing the potential number of keypoints in the cloned region. Results using maximally stable extremal regions' features, speeded up robust features, and scale-invariant feature transform show a very significant improvement over the state of the art, particularly when different descriptors are combined. A statistical filtering step is also proposed, increasing the homogeneity of the clusters and thereby improving the results. Finally, our methodology uses an adaptive technique for independently selecting the optimal k value for each image, allowing our method to work well when there are multiple cloned regions. We also show that our methodology works well when the training and testing datasets are mismatched. © 2015 SPIE and IS&T [Alfraih, Briffa, and Wesemeyer: Cloning localization approach using k -means clustering. . . Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 08/25/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Alfraih, Briffa, and Wesemeyer: Cloning localization approach using k -means clustering. . . Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 08/25/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Alfraih, Briffa, and Wesemeyer: Cloning localization approach using k -means clustering. . . Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 08/25/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Alfraih, Briffa, and Wesemeyer: Cloning localization approach using k -means clustering. . . Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 08/25/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Alfraih, Briffa, and Wesemeyer: Cloning localization approach using k -means clustering. . . Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 08/25/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Alfraih, Briffa, and Wesemeyer: Cloning localization approach using k -means clustering. . . Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 08/25/2015 Terms of Use: http://spiedigital...