Deepfake detection based on 3D face data augmentation
Wang Haoran1, Yang Minmin2，Wang Zeyuan1，Bai Liang1，Yu Tianyuan1，Guo Yanming1
(1.College of System Engineering, National University of Defence Technology, Changsha 410073, China; 2.School of Information and Electonics Technology， Jiamusi University,Jiamusi 156100, China)
Abstract： With the development of deepfake technology, the production and dissemination of deepfake videos have become increasingly easy, posing significant security risks to society. Therefore, researching deepfake detection algorithms has become an important direction in the field of network security. This paper focuses on proposing a deepfake detection algorithm with better generalization performance, higher efficiency, and stronger interpretability. Experiments are conducted on three deepfake video datasets: DFDC, FaceForensic++, and CelebDF. Firstly, the Multitask Cascaded Convolutional Networks (MTCNN) face detection algorithm is used to extract facial images. Then, the EfficientNet network is combined with the Transformer architecture as the detection model. The model is trained and tested using data augmentation, attention mechanisms, and globallocal fusion methods. Without employing complex model ensembles or knowledge distillation, our model achieves comparable detection performance to stateoftheart methods.
Key words : deep forgery detects;attention-mechanism; data augmentation; neural networks
