Humans Versus Machines: A Deepfake Detection Faceoff
ABSTRACT Machine learning (ML) models for deepfake detection are important for countering the threat of such videos. However, human detection is also critical because automated approaches may not always be available to people online. This study compares ML models versus humans for deepfake detection. Results surprisingly showed that humans performed better. Implications of our work are discussed.
Authors
- Dion Hoe‐Lian Goh (ORCID: https://orcid.org/0000-0003-2904-3269)
- Chei Sian Lee (ORCID: https://orcid.org/0000-0003-0891-6526)
- Jonathan Pan
Institutions
- Nanyang Technological University (SG)
Publication Details
- Journal
- Proceedings of the Association for Information Science and Technology
- Published
- 2024-10-01
- DOI
- https://doi.org/10.1002/pra2.1139
- Citations
- 3
- Primary Topic
- Generative Adversarial Networks and Image Synthesis
- Type
- article
- Field-Weighted Citation Impact
- 0.75
Funders
- Ministry of Education - Singapore
- Ministry of Education, India