EAGER: Image and Video Forensics: Detecting Image Manipulation by Content Analysis

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The advent of sophisticated photo editing software has made it increasingly easier to manipulate digital images. Often visual inspection cannot definitively distinguish the resulting forgeries from authentic photographs. In response, forensic techniques have emerged to detect geometric or statistical inconsistencies that result from specific forms of photo manipulation. The PI aspires to develop new forensic methods based on geometric content analysis, which focus on finding inconsistencies in the geometric relationships among objects depicted in a photograph. The geometric relationships in a 2D image correspond to the projection of the relations that exist in the 3D scene; if a scene is known to contain a given relationship but the projected relation does not hold in the photograph, then one may conclude that the photograph is not a true projective image of the scene. With this in mind, the PI's goal in this exploratory project is to build a set of testable constraints that must be satisfied in real images, so that an unsatisfied constraint constitutes definitive, objective evidence of image manipulation. Fundamental challenges of this work include: developing tools for analysis from incomplete lighting information, building testable models of skin reflectance, accounting for structured uncertainty in feature comparison, and establishing method guidelines for forensic image analysis.Broader Impacts: This project will create tools for objectively detecting image manipulation, which will help reporters, law enforcement, scientists, and others differentiate between legitimate photographs and forged images. The products of this research will be communicated via academic publications and online source code. Collaborations with industrial partners will allow the research to have practical impact as well.