Deepfake Detection: A Tutorial
Computing Sciences and Computer Engineering
This tutorial presents developments on the detection of Deepfakes, which are realistic images, audios and videos created using deep learning techniques. Deepfakes can be readily used for malicious purposes and pose a serious threat to privacy and security. The tutorial summarizes recent Deepfake detection techniques and evaluates their effectiveness with respect to several benchmark datasets. Our study finds that no single method can reliably detect all Deepfakes and, therefore, combining multiple methods is often necessary to achieve high detection rates. The study also suggests that more extensive and diverse datasets are needed to improve the accuracy of detection algorithms. A taxonomy of Deepfake detection techniques is introduced to aid future research and development in the field. We conclude by calling for the development of more effective Deepfake detection methods and countermeasures to combat this evolving and spreading threat.
IWSPA '23: Proceedings of the 9th ACM International Workshop On Security and Privacy Analytics
Sung, A. H.
(2023). Deepfake Detection: A Tutorial. IWSPA '23: Proceedings of the 9th ACM International Workshop On Security and Privacy Analytics.
Available at: https://aquila.usm.edu/fac_pubs/21004