Deep learning for digital image forensics

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Digital media data becomes increasingly easier to be recorded, edited and shared. However, this also leads toserious security and forensics concerns of digital media data. Image forensics has been an active research areain the past decade, while anti-forensic techniques have also evolved in response to the rapid development ofimage forensics tools because the forgers also attempt to identify weaknesses of image forensics and fool theforensic investigators by employing anti-forensic techniques. The proposed research project brings togetherresearchers from UBC and Huawei Canada with the goal of developing deep learning based forensic methodsto learn feature representations from weak forensic trace signals and fulfill classification automatically. Morespecifically, we focus on the following two technical goals:1) develop a deep learning based framework forimage inpainting forensics; and 2) propose a multi-purpose convolutional neural network (CNN) frameworkfor simultaneous detection of multiple types of image manipulations.