The widespread dissemination of Deepfakes demands effective approaches that can detect perceptually convincing forged images. In this paper, we aim to capture the subtle manipulation artifacts at different scales using transformer models. In particular, we introduce a Multi-modal Multi-scale TRansformer (M2TR), which operates on patches of different sizes to detect local inconsistencies in images at different spatial levels. M2TR further learns to detect forgery artifacts in the frequency domain to complement RGB information through a carefully designed cross modality fusion block. In addition, to stimulate Deepfake detection research, we introduce a high-quality Deepfake dataset, SR-DF, which consists of 4,000 DeepFake videos generated by state-of-the-art face swapping and facial reenactment methods. We conduct extensive experiments to verify the effectiveness of the proposed method, which outperforms state-of-the-art Deepfake detection methods by clear margins.
@article{arxiv.2104.09770,
title = {M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection},
author = {Junke Wang and Zuxuan Wu and Wenhao Ouyang and Xintong Han and Jingjing Chen and Ser-Nam Lim and Yu-Gang Jiang},
journal= {arXiv preprint arXiv:2104.09770},
year = {2022}
}