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CVPR
2010
IEEE

Learning from Interpolated Images using Neural Networks for Digital Forensics

13 years 12 months ago
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.
Yizhen Huang, Na Fan
Added 04 Apr 2010
Updated 14 May 2010
Type Conference
Year 2010
Where CVPR
Authors Yizhen Huang, Na Fan
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