DFT-MF: Enhanced deepfake detection using mouth movement and transfer learning
Ammar Elhassan, Mohammad Al-Fawa’reh, Mousa Tayseer Jafar, Mohammad Ababneh, Shifaa Tayseer Jafar
Abstract
Deepfake technology presents serious cybersecurity challenges that have become more prevalent with the availability of easily accessible applications. An effective method for detection and prevention of this is necessary. This paper introduces a robust approach and software implementation to detect fake videos constructed with Deep Learning technology that depends on utilizing teeth and mouth movement as distinguishing features that remain very difficult to perfect when faking videos. The proposed methodology has a higher efficiency and accuracy of fake video detection than similar approaches. The work in this article is an extension of previous work that introduced the main concepts with additional application of multi-transfer learning approaches including DenseNet121, DenseNet169, EfficientNetB0, EfficientNetB7, InceptionV3, MobileNet, ResNet50, vgg16, vgg19 and Xception to enhance the algorithm's ability to detect and classify Deepfake videos based on features extracted from the teeth and mouth frames as a biological signal.