| Title | DeepFake Video Analysis using SIFT Features |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Đorđević, M., Milivojević, M., Gavrovska, A. |
| Conference Name | 2019 27th Telecommunications Forum (℡FOR) |
| Keywords | artificial intelligence, computer vision., Deep Learning, DeepFake, DeepFake algorithms, deepfake analysis, DeepFake video analysis, Deepfakes, Forgery, Human Behavior, human factors, learning (artificial intelligence), Metrics, neural nets, pubcrawl, resilience, Resiliency, Scalability, scale-invariant feature transform, SIFT features, still images, Transforms, video analysis, video clips, video signal processing, visual information |
| Abstract | Recent advantages in changing faces using DeepFake algorithms, which replace a face of one person with a face of another, truly represent what artificial intelligence and deep learning are capable of. Deepfakes in still images or video clips represent forgeries and tampered visual information. They are becoming increasingly successful and even difficult to notice in some cases. In this paper we analyze deepfakes using SIFT (Scale-Invariant Feature Transform) features. The experimental results show that in deepfake analysis using SIFT keypoints can be considered valuable. |
| DOI | 10.1109/℡FOR48224.2019.8971206 |
| Citation Key | dordevic_deepfake_2019 |
DeepFake Video Analysis using SIFT Features