SpliceRadar: A Learned Method For Blind Image Forensics

원문 초록

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.