Abstract
Recently, various Deepfake detection methods have been proposed, and most of them are based on convolutional neural networks (CNNs). These detection methods suffer from overfitting on the source dataset and do not perform well on cross-domain datasets which have different distributions from the source dataset. To address these limitations, a new method named FeatureTransfer is proposed in this paper, which is a two-stage Deepfake detection method combining with transfer learning. Firstly, The CNN model pretrained on a third-party large-scale Deepfake dataset can be used to extract the more transferable feature vectors of Deepfake videos in the source and target domains. Secondly, these feature vectors are fed into the domain-adversarial neural network based on backpropagation (BP-DANN) for unsupervised domain adaptive training, where the videos in the source domain have real or fake labels, while the videos in the target domain are unlabelled. The experimental results indicate that the proposed method FeatureTransfer can effectively solve the overfitting problem in Deepfake detection and greatly improve the performance of cross-dataset evaluation.
Highlights
The Deepfake video generation technology has attracted much attention, especially the popular Deepfake application called “ZAO”. e application requires the user to provide a clear personal face image and complete facial feature verification, but the image collection protocol is not user-friendly. e majority of users express anxiety about the security of face information
To make the Deepfake video detection method more robust on cross-domain datasets, this paper proposes a new method called FeatureTransfer, which is based on unsupervised domain adaptation
Results and Analysis. e proposed method is compared with previous Deepfake detection methods, including Xception [16], FSSpotter [18], Face X-Ray [22], and se_resnext101_32 × 4 d [37]. e cross-domain Deepfake detection results are exhibited in terms of AUC and ERR on recently released datasets, such as DF-TIMIT, FF-FS, DFD, DFDC-P, and Celeb-DF. e pretrained weight provided by
Summary
The Deepfake video generation technology has attracted much attention, especially the popular Deepfake application called “ZAO”. e application requires the user to provide a clear personal face image and complete facial feature verification, but the image collection protocol is not user-friendly. e majority of users express anxiety about the security of face information. The Deepfake technology could be used to create fake news, posing threats to user privacy and social security [1,2,3,4,5,6]. Us, it is critical to detect the Deepfake images or videos for face forensics. E goal of face forensics is to detect whether a face in image or video has been created or manipulated. E Deepfake video detection method mainly uses deep learning technology, which is usually composed of two parts: face detection and classification. Yang et al [12] detected videos Deepfake through the cue of inconsistent head poses. Li et al [13] exposed Deepfake videos by detecting face warping artifacts
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