Investigation of deep fake video detection
Sarah Riyadh Adnan, Huda Abdulaali Abdulbaqi · AIP conference proceedings · 2024
The rapid developments of deep learning techniques are the process that can easily create and use a fake media known as Deepfakes.Deep learning techniques can generate facial motion transferring, gender-changing, and face swapping with others.Currently, it is possible to create hyper-realistic digital media like images and videos through deep learning techniques that are easily accessible.However, that becomes a threat for everyone, if used for harmful purposes.This work presents how deep learning is used for Deepfake generation and detection.There are two methods that are most popular for Deepfake generation: Generative Adversarial Networks (GANs) and autoencoder techniques that is presented in this work.It also, introduces a survey of some researchers' works and methods that used for Deepfake detection during the recent years.This paper proposed a method to discriminate if that video is real or fake by using a Support Vector Machine (SVM) classifier, the method presented in this work for deepfake detection is based on Gray Level Co-occurrence Matrix (GLCM) as a feature extraction method applying on 4 bands to obtain 13 feature form each one to achieve good accuracy of the classifier with a score 70.47% that batter than previous work that didn't exceed 60% of accuracy.