Recaptured image forensics based on local ternary count of high order prediction error

With the advancement in image display technology and the widespread use of various image acquisition devices, recapturing high-quality images from high-fidelity LCD (liquid crystal display) screens becomes very convenient and easy. These recaptured images pose serious threats on current image forensic technology and intelligent recognition systems. In order to solve these security problems, we propose a recaptured image forensics method based on local ternary count (LTC) of high order prediction error. Specifically, given an image, the second-order difference maps on different directions are calculated in HSV color space for the original image and its average pooled version. Then, we calculate the prediction error maps from these second-order difference maps through a linear predictor. Finally, the normalized LTC histograms of all the downsampled versions of the obtained prediction error maps are calculated as features for training and testing. Single database experiments demonstrate that our proposed method performs very similar with the state-of-the-art traditional method on the most difficult-to-detect ICL-COMMSP database with obvious less execution-time and lower feature dimension. Meanwhile, it performs very close to the existing well-performed deep learning based method on all the databases, which verifies the effectiveness of our proposed features. Besides, mixed database experiments verify the superiority on generalization ability of our proposed method. Moreover, it is robust to JPEG compression.