Media Forensics Using Machine Learning Approaches

Consumer-grade imaging sensors have become ubiquitous in the past decade. Images and videos, collected from such sensors are used by many entities for public and private communications, including publicity, advocacy, disinformation, and deception.

In this thesis, we present tools to be able to extract knowledge from and understand this imagery and its provenance. Many images and videos are modified and/or manipulated prior to their public release. We also propose a set of forensics and counter-forensic techniques to determine the integrity of this multimedia content and modify it in specific ways to deceive adversaries. The presented tools are evaluated using publicly available datasets and independently organized challenges.