Image Feature Detectors for Deepfake Video Detection
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 suc...
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Published in | 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA) pp. 1 - 4 |
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Main Authors | , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.11.2019
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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ISSN: | 2161-5330 |
DOI: | 10.1109/AICCSA47632.2019.9035360 |