Open AccessDeepFake Video Detection through Facial Sparse Optical Flow based Light CNN
Author(s) -
Shuya Fang,
Shucheng Wang,
Rongjun Ye
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2224/1/012014
Subject(s) - computer science , spare part , metric (unit) , optical flow , artificial intelligence , image (mathematics) , resource (disambiguation) , pattern recognition (psychology) , machine learning , computer vision , computer network , operations management , marketing , economics , business
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.