Identifying natural images and computer generated graphics based on binary similarity measures of PRNU

Aiming at the identification of natural images and computer generated graphics, an image source pipeline forensics method based on binary similarity measures of PRNU (photo response non-uniformity) is proposed. As PRNU is a unique attribute of natural images, binary similarity measures of PRNU are used to represent the differences between natural images and computer generated graphics. Binary Kullback-Leibler distance, binary minimum histogram distance, binary absolute histogram distance and binary mutual entropy are calculated from PRNU in RGB three channels. With a total of 36 dimensions of features, LIBSVM is used for classification. Experimental results and analysis indicate that it can achieve an average identification accuracy of 99.83%, and the capability of identifying natural images and computer generated graphics is balanced. Meanwhile, it is robust against JPEG compression, rotation and additive noise.