Please use this identifier to cite or link to this item:
http://arks.princeton.edu/ark:/88435/dsp01sj1394986| Title: | Deepfakes Detection |
| Authors: | Soulmani, Radia |
| Advisors: | Mayer, Jonathan |
| Department: | Computer Science |
| Class Year: | 2020 |
| Abstract: | There has been a wide increase of fake news through different channels, many social media platforms are looking to limit this growth. We propose a new solution to help tackle deepfakes, a popular form of fake news which can present a big challenge to fair reporting due to their ease of spread and their close representation of reality. This solution relies on research on human eyeblink patterns and how they compare to trends seen on deepfaked videos. The results of these studies provide an angle for deepfakes detection that we explore through this project, using computer vision tools. |
| URI: | http://arks.princeton.edu/ark:/88435/dsp01sj1394986 |
| Access Restrictions: | Walk-in Access. This thesis can only be viewed on computer terminals at the Mudd Manuscript Library. |
| Type of Material: | Princeton University Senior Theses |
| Language: | en |
| Appears in Collections: | Computer Science, 1988-2020 |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| SOULMANI-RADIA-THESIS.pdf | 628.74 kB | Adobe PDF | Request a copy |
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