Reference number : GC-2017-Q4-01867, amendment number : , agreement type : G, recipient legal name : Wang, Z. Jane (The University of British Columbia), recipient country : CA, recipient province : nan, recipient city : British Columbia|Colombie-Britannique, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Digital media data becomes increasingly easier to be recorded, edited and shared. However, this also leads to_x000D_
serious security and forensics concerns of digital media data. Image forensics has been an active research area_x000D_
in the past decade, while anti-forensic techniques have also evolved in response to the rapid development of_x000D_
image forensics tools because the forgers also attempt to identify weaknesses of image forensics and fool the_x000D_
forensic investigators by employing anti-forensic techniques. The proposed research project brings together_x000D_
researchers from UBC and Huawei Canada with the goal of developing deep learning based forensic methods_x000D_
to learn feature representations from weak forensic trace signals and fulfill classification automatically. More_x000D_
specifically, we focus on the following two technical goals:1) develop a deep learning based framework for_x000D_
image inpainting forensics; and 2) propose a multi-purpose convolutional neural network (CNN) framework_x000D_
for simultaneous detection of multiple types of image manipulations., program purpose french : Digital media data becomes increasingly easier to be recorded, edited and shared. However, this also leads to_x000D_
serious security and forensics concerns of digital media data. Image forensics has been an active research area_x000D_
in the past decade, while anti-forensic techniques have also evolved in response to the rapid development of_x000D_
image forensics tools because the forgers also attempt to identify weaknesses of image forensics and fool the_x000D_
forensic investigators by employing anti-forensic techniques. The proposed research project brings together_x000D_
researchers from UBC and Huawei Canada with the goal of developing deep learning based forensic methods_x000D_
to learn feature representations from weak forensic trace signals and fulfill classification automatically. More_x000D_
specifically, we focus on the following two technical goals:1) develop a deep learning based framework for_x000D_
image inpainting forensics; and 2) propose a multi-purpose convolutional neural network (CNN) framework_x000D_
for simultaneous detection of multiple types of image manipulations., agreement title english : Deep learning for digital image forensics, agreement title french : Deep learning for digital image forensics, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-03-07, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada