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Media Forensics Using Machine Learning Approaches

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thesis
posted on 30.10.2019, 16:34 by David Güera
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.

Funding

Defense Advanced Research Projects Agency (DARPA) and the Air Force Research Laboratory (AFRL) under agreement number FA8750-16-2-0173

History

Degree Type

Doctor of Philosophy

Department

Electrical and Computer Engineering

Campus location

West Lafayette

Advisor/Supervisor/Committee Chair

Edward J. Delp

Additional Committee Member 2

Mary L. Comer

Additional Committee Member 3

Amy R. Reibman

Additional Committee Member 4

Fengqing M. Zhu

Licence

Exports