Learning from interpolated images using neural networks for digital forensics

Interpolated images have data redundancy, and special correlation exists among neighboring pixels, which is a crucial clue in digital forensics. We design a neural network based framework to approximate the stylized computational rules of interpolation algorithms for learning statistical inter-pixel correlation of interpolated images. The interpolation process is cognized from the interpolation results. Experiments are carried out on camera built-in Color Filter Array interpolation and super resolution: Three classifiers are trained to classify image interpolation algorithms, identify source cameras and uncover digital forgeries. Like the Wiener attack in watermarking, the special correlation can be reduced or transferred it to another image by our learned network.