Deepfake Detection With Combined Unsupervised-Supervised Contrastive Learning
Junshuai Zheng, Yichao Zhou, Xiyuan Hu, Zhenmin Tang · 2024
The malicious dissemination of fake images has caused a societal trust crisis, deepfake detection becomes a hot topic now. Through existing detection methods achieve good results in intra-dataset, their performance are poor for unknown manipulations or datasets. To deal with this problem, this paper proposes a new deepfake detection model with combined unsupervised-supervised contrastive learning. By combining unsupervised contrastive learning and supervised contrastive learning with deepfake detection together, the model can discover the essence of fake images from both individual and class features. In addition, a multi-scale attention fusion module is proposed, which helps to enhance the model stability by fusion global and local features of the image. Finally, lots of experiments prove that our method has good performance and generalization ability in intra-dataset, cross-dataset and cross-manipulation scenarios.