Date of Award
Spring 5-2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Cyber Defense (PhDCD)
First Advisor
Ashley Podhradsky
Second Advisor
Arica Klum
Third Advisor
William Bendix
Fourth Advisor
Andrew Kramer
Abstract
Traditionally, image forgeries were limited to defraud art collectors and falsify documents. The rise of visual media provided legitimate reasons for artists to forge images in the pursuit of more effective storytelling. In all cases these methods required specialized domain knowledge and highly skilled artists. The rise of computers simplified many of these processes, lowering the bar in time, materials, and skill, and thus giving most users access basic tools for altering images. Today, image alteration tools leverage machine learning and artificial intelligence techniques, and can generate incredibly accurate image forgeries. Effective methods for verifying the authenticity of media data are increasingly needed. This work seeks to identify and explain methods for identifying video image forgeries, more commonly known as “deepfakes.”
Recommended Citation
Johnson, Chad R., "Forensic Iconography for Image Forgery Detection" (2024). Masters Theses & Doctoral Dissertations. 462.
https://scholar.dsu.edu/theses/462