  With technological advances leading to an increase in mechanisms for image
tampering, fraud detection methods must continue to be upgraded to match their
sophistication. One problem with current methods is that they require prior
knowledge of the method of forgery in order to determine which features to
extract from the image to localize the region of interest. When a machine
learning algorithm is used to learn different types of tampering from a large
set of various image types, with a large enough database we can easily classify
which images are tampered. However, we still are left with the question of
which features to train on, and how to localize the manipulation. In this work,
deep learning for object detection is adapted to tampering detection to solve
these two problems, while fusing features from multiple classic techniques for
improved accuracy. A Multi-stream version of the Faster RCNN network will be
employed with the second stream having an input of the element-wise sum of the
ELA and BAG error maps to provide even higher accuracy than a single stream
alone.
