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Kharbat, F. F., Elamsy, T., Mahmoud, A., Abdullah, R..  2019.  Image Feature Detectors for Deepfake Video Detection. 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA). :1—4.
Detecting DeepFake videos are one of the challenges in digital media forensics. This paper proposes a method to detect deepfake videos using Support Vector Machine (SVM) regression. The SVM classifier can be trained with feature points extracted using one of the different feature-point detectors such as HOG, ORB, BRISK, KAZE, SURF, and FAST algorithms. A comprehensive test of the proposed method is conducted using a dataset of original and fake videos from the literature. Different feature point detectors are tested. The result shows that the proposed method of using feature-detector-descriptors for training the SVM can be effectively used to detect false videos.