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Local descriptors based on the image noise residual have proven extremelyeffective for a number of forensic applications, like forgery detection andlocalization. Nonetheless, motivated by promising results in computer vision,the focus of the research community is now shifting on deep learning. In thispaper we show that a class of residual-based descriptors can be actuallyregarded as a simple constrained convolutional neural network (CNN). Then, byrelaxing the constraints, and fine-tuning the net on a relatively smalltraining set, we obtain a significant performance improvement with respect tothe conventional detector.

Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection