A Novel Image Splicing Detection Algorithm Based on Generalized and Traditional Benford’S Law

Due to the ease of access to platforms that can be used by forgers to tamper digital documents, providing automatic tools for identifying forged images is now a hot research field in image processing. this paper presents a novel forgery detection algorithm based on variants of benford's law. in the proposed method, mean absolute deviation (mad) feature is extracted using traditional benford's law. also, generalized benford's law is used for mantissa distribution feature vector. in addition to benford's law-based features, other statistical features are used to construct the final feature vector. finally, support vector machine (svm) with three different kernel functions is used to classify original and forged images. the method has been tested on two common image datasets (casia v1.0 and v2.0). the experimental results show that 0.27% and 0.21% improvements on casia v1.0 and casia v2.0 datasets were achieved, respectively in detection accuracy by the proposed method in comparison to best state-of-the-art methods. the proposed efficient algorithm has a simple implementation. moreover, on the basis of benford’s law rich features are extracted from images so that classification process is efficiently performed by a simple svm classifier in a short time.