Image forgery localization through the fusion of camera-based, feature-based and pixel-based techniques

We propose an image forgery localization technique which fuses the outputs of three complementary tools, based on sensor noise, machine-learning and block-matching, respectively. To apply the sensor noise tool, a preliminary camera identification phase was required, followed by estimation of the camera fingerprint, and then forgery detection and localization. The machine-learning is based on a suitable local descriptor, while block-matching relies on the PatchMatch algorithm. A decision fusion strategy is then implemented, based on suitable reliability indexes associated with the binary masks. The proposed technique ranked first in phase 2 of the first Image Forensics Challenge organized in 2013 by the IEEE Information Forensics and Security Technical Committee (IFS-TC).