宁夏大学学报（自然科学版）2024，Vol.45Issue(3)：299-306,8.
一种特征融合的双流深度检测伪造人脸方法
A Feature Fusion Dual-Stream Deepfake Detection Method for Forged Faces
摘要
Abstract
The rapid advancement of Deepfake technology has rendered deepfake video and audio content increasingly realistic,with widespread applications in political forgery,financial fraud,and the dissemination of fake news.Therefore,the research and development of efficient Deepfake detection methods have become cru-cial.This study explores a strategy that combines Vision Transformers(ViT)with Convolutional Neural Net-works(CNN),leveraging the advantages of CNN in local feature extraction and the potential of ViT in model-ing global relationships to enhance the performance of Deepfake detection algorithms in practical applications.Moreover,to strengthen the model's resilience against the impacts of image or video compression,frequency domain features are introduced,and a dual-stream network is employed to extract features,thereby improving detection performance and stability across compressed scenarios.Experimental results indicate that the dual-stream network model based on multi-domain feature fusion demonstrates commendable detection performance on the FaceForensics++dataset,achieving an ACC value of 96.98% and an AUC value of 98.82% .Satis-factory results are also obtained in cross-dataset detection,with an AUC value of 75.41% on the Celeb-DF dataset.关键词
Deepfake检测/CNN结合ViT/RGB频域特征融合/跨压缩场景Key words
Deepfake detection/CNN combined with ViT/RGB frequency domain feature fusion/cross-compression scenarios分类
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孟媛,汪西原..一种特征融合的双流深度检测伪造人脸方法[J].宁夏大学学报（自然科学版）,2024,45(3):299-306,8.基金项目
国家自然科学基金资助项目(42361056) （42361056）
