  Detection and localization of image manipulations like splices are gaining in
importance with the easy accessibility of image editing softwares. While
detection generates a verdict for an image it provides no insight into the
manipulation. Localization helps explain a positive detection by identifying
the pixels of the image which have been tampered. We propose a deep learning
based method for splice localization without prior knowledge of a test image's
camera-model. It comprises a novel approach for learning rich filters and for
suppressing image-edges. Additionally, we train our model on a surrogate task
of camera model identification, which allows us to leverage large and widely
available, unmanipulated, camera-tagged image databases. During inference, we
assume that the spliced and host regions come from different camera-models and
we segment these regions using a Gaussian-mixture model. Experiments on three
test databases demonstrate results on par with and above the state-of-the-art
and a good generalization ability to unknown datasets.

Computer Vision and Pattern Recognition