Shannon Gallagher
SEI CERT
Shannon Gallagher is a data scientist in the SEI’s CERT Division. In that role, Gallagher focuses on modeling, uncertainty quantification, and data visualization and leading research to develop a statistical pipeline to help determine the authenticity of images and videos. Prior to joining the SEI, Gallagher worked as a post-doctoral researcher at the National Institute of Allergy and Infectious Diseases. There she worked on statistical modeling of infectious diseases, competing events analysis for the ACTT-1 COVID-19 trial, and analysis of statistical tests in low event rate settings. Gallagher received a doctoral degree in statistics from Carnegie Mellon University (CMU). While at CMU, Gallagher was a research and teaching assistant and served as president of the Women in Statistics group.
Publications by Shannon Gallagher
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A Machine Learning Pipeline for Deepfake Detection
November 11, 2022 • Presentation
Shannon Gallagher
The aim of this project is to develop a deepfake detection prototype framework with at least 85% accuracy.
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Machine Learning for Deepfake Detection
November 07, 2022 • Presentation
Shannon Gallagher
This presentation by Shannon Gallagher was delivered virtually at Deepfakes Day 2022 on August 30, 2022.
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Deepfakes 101
November 07, 2022 • Presentation
Shannon GallagherTom Scanlon
This presentation by Shannon Gallagher and Thomas Scanlon was delivered virtually at Deepfakes Day 2022 on August 30, 2022.
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Preview of A Machine Learning Pipeline for Deepfake Detection
November 07, 2022 • Video
Shannon Gallagher
This short video provides an introduction to a research topic presented at the SEI Research Review 2022.
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A Dive into Deepfakes
August 18, 2022 • Podcast
Shannon GallagherDominic A. Ross
Shannon Gallagher, a data scientist with SEI’s CERT Division, and Dominic Ross, multimedia team lead for the SEI, discuss deepfakes, their exponential growth in recent years, and their increasing technical sophistication and realism.
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