A Distributed Local Margin Learning based scheme for high-dimensional feature processing in image tampering detection

With the development of image tampering detection, more and more features are involved to improve the detection rate, and nowadays the high-dimensional features based methods get the state-of-the-art detection accuracies. However, the high dimensionality will cause excessive time cost in the classification phase, moreover it would probably introduce redundant features which will confuse the classifier. An effective scheme based on Distributed Local Margin Learning (D-LML) is proposed in this paper to solve the problems caused by the high-dimensionality of features in the image tampering detection work. Local Margin Learning algorithm distributed to different clients is employed to rank the importance of the original features, and we can get features with low dimensionality by preserving the important features while excluding the insignificant features. Experimental results show that the D-LML method could greatly reduce the dimensionality of the original features, and keep the detection rates fluctuating in a relatively small range.