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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Bibliographic Details
Published in2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA) pp. 1 - 4
Main Authors Kharbat, Faten F., Elamsy, Tarik, Mahmoud, Ahmed, Abdullah, Rami
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2019
Subjects
BRISK
Classification algorithms
DeepFake Video
Detectors
FAST
Feature extraction
HOG
Information integrity
KAZE
ORB
Support vector machines
SURF
Training
Videos
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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.
ISSN:2161-5330
DOI:10.1109/AICCSA47632.2019.9035360