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Camera fingerprints are precious tools for a number of image forensics tasks.A well-known example is the photo response non-uniformity (PRNU) noise pattern,a powerful device fingerprint. Here, to address the image forgery localizationproblem, we rely on noiseprint, a recently proposed CNN-based camera modelfingerprint. The CNN is trained to minimize the distance between same-modelpatches, and maximize the distance otherwise. As a result, the noiseprintaccounts for model-related artifacts just like the PRNU accounts fordevice-related non-uniformities. However, unlike the PRNU, it is only mildlyaffected by residuals of high-level scene content. The experiments show thatthe proposed noiseprint-based forgery localization method improves over thePRNU-based reference.

Camera-based Image Forgery Localization using Convolutional Neural Networks