Detecting manipulated images has become an important problem in many domains (including medical imaging, forensics, journalism and scientific publication) largely due to the recent success of image synthesis techniques and the accessibility of image editing software. Many previous signal-processing techniques are concerned about finding forgery through simple transformation (e.g. resizing, rotating, or scaling), yet little attention is given to examining the semantic content of an image, which is the main issue in recent image forgeries. Here, we present a complete workflow for finding the anomalies within images by combining the methods known in computer graphics and artificial intelligence. We first find perceptually meaningful regions using an image segmentation technique and classify these regions based on image statistics. We then use AI common-sense reasoning techniques to find ambiguities and anomalies within an image as well as perform reasoning across a corpus of images to identify a semantically based candidate list of potential fraudulent images. Our method introduces a novel framework for forensic reasoning, which allows detection of image tampering, even with nearly flawless mathematical techniques.
All Science Journal Classification (ASJC) codes
- Pathology and Forensic Medicine
- Information Systems
- Computer Science Applications
- Medical Laboratory Technology