DeepFake Video Detection through Facial Sparse Optical Flow based Light CNN

Abstract

DeepFake detection has become an attractive research topic with tremendous growth of interests recently. However, existing DeepFake detection studies spare no effort to improve accuracy or Area Under Curve metric, regardless of computing costs. In this work, the tradeoff between result accuracy and computing resources is taken into consideration. A facial sparse optical flow method is proposed to extract spatio-temporal features representing facial expression incoherence, which helps to distinguish fake videos and real videos. The features fed into a light CNN model are highly compact and low-dimensional. The proposed method has an amazing small amount of parameters with high training speed and low usage of GPU memory. The low resource requirement makes it possible to port to embedded development platform.

Deploying Facial Segmentation Landmarks for Deepfake Detection

“…In Deepfake detection methods, face detection is the first step [27], [28], for that, it is important to use an excellent technique to determine whether the image contains a face or not [29]. The face detection process in this study was performed using the deep MediaPipe framework [15].…”