中圖分類號：KN 90
文獻(xiàn)標(biāo)識碼：A
DOI:10.19358/j.issn.2097-1788.2023.09.003
引用格式：王昊冉,，楊敏敏,，王澤源，等.基于三維人臉數(shù)據(jù)增強的深度偽造檢測方法［J］.網(wǎng)絡(luò)安全與數(shù)據(jù)治理,，2023,，42（9）：11-20.

Deepfake detection based on 3D face data augmentation

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 CelebDF. Firstly, the Multitask 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 globallocal fusion methods. Without employing complex model ensembles or knowledge distillation, our model achieves comparable detection performance to stateoftheart methods.

Key words : deep forgery detects;attention-mechanism; data augmentation; neural networks