Based on the sparse representation of images

a new approach to distinguish photographic images and photorealistic computer graphics is proposed. The proposed approach is robust enough to compression to guarantee image authenticity forensics. The Tetrolet transformation chooses the optimal tetromino partition for each 4×4 image block in terms of the minimal L-norm criterion to protect local image geometry structure and to obtain the sparsest image representation. When observing the adaptive values c

an image is represented as a normalized histogram with 117 bins corresponding to the number of occurrences of different block covering

i.e. the features of HoC (Histogram of Covering). The experimental results demonstrate the HoC features extracted from S (saturation) are able to characterize the distinct statistical properties in the local geometry between photographic images and photorealistic computer graphics. The proposed approach is applicable to image authenticity detection and auto-classification.