Digital images are everywhere - from our cell phones to the pages of our online news sites. How we choose to use digital image processing raises a surprising host of legal and ethical questions that we must address. What are the ramifications of hiding data within an innocent image? Is this an intentional security practice when used legitimately, or intentional deception? Is tampering with an image appropriate in cases where the image might affect public behavior? Does an image represent a crime, or is it simply a representation of a scene that has never existed? Before action can even be taken on the basis of a questionable image, we must detect something about the image itself. Investigators from a diverse set of fields require the best possible tools to tackle the challenges presented by the malicious use of today's digital image processing techniques.

In this work, we are exploring the emerging field of digital image forensics, including the topic areas of source camera identification, forgery detection, provanance analysis, steganalysis, and semantic content analysis. In source camera identification, we seek to identify the particular model of a camera, or the exact camera, that produced an image. Forgery detection's goal is to establish the authenticity of an image, or to expose any potential tampering the image might have undergone. Provenance analysis attempts to trace the source and lineage of an image or video, especially after it has undergone several rounds of edits. With steganalysis, the detection of hidden data within an image is performed, with a possible attempt to recover any detected data. And finally, the semantic content analysis we work on finds patterns in the messages conveyed by a collection of media objects.

This work was supported by Air Force STTR Award No. FA9550-05-C-0172, DARPA and Air Force Research Laboratory (AFRL) agreement numbers FA8750-16-2-0173 and FA8750-20-2-1004, USAID agreement number 7200AA18CA00054, and the NVIDIA Corporation.