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With technological advances leading to an increase in mechanisms for imagetampering, fraud detection methods must continue to be upgraded to match theirsophistication. One problem with current methods is that they require priorknowledge of the method of forgery in order to determine which features toextract from the image to localize the region of interest. When a machinelearning algorithm is used to learn different types of tampering from a largeset of various image types, with a large enough database we can easily classifywhich images are tampered. However, we still are left with the question ofwhich features to train on, and how to localize the manipulation. In this work,deep learning for object detection is adapted to tampering detection to solvethese two problems, while fusing features from multiple classic techniques forimproved accuracy. A Multi-stream version of the Faster RCNN network will beemployed with the second stream having an input of the element-wise sum of theELA and BAG error maps to provide even higher accuracy than a single streamalone.

Deep Localization of Mixed Image Tampering Techniques