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What is DeepFake Detection with Vision Transformer about?

This paper presents an improved vision transformer model for DeepFake detection that effectively extracts both local and global features, addressing the limitations of traditional convolutional neural networks (CNNs) which struggle with overfitting and spatial relationships. The proposed model outperforms state-of-the-art (SOTA) models on the DFDC test dataset, achieving higher AUC and f1 scores by utilizing a combination of CNN features and patch-based positioning, along with a distillation technique for e

Author

sawais.scholar

Language

EN