  Recently, Deepfake has drawn considerable public attention due to security
and privacy concerns in social media digital forensics. As the wildly spreading
Deepfake videos on the Internet become more realistic, traditional detection
techniques have failed in distinguishing between real and fake. Most existing
deep learning methods mainly focus on local features and relations within the
face image using convolutional neural networks as a backbone. However, local
features and relations are insufficient for model training to learn enough
general information for Deepfake detection. Therefore, the existing Deepfake
detection methods have reached a bottleneck to further improve the detection
performance. To address this issue, we propose a deep convolutional Transformer
to incorporate the decisive image features both locally and globally.
Specifically, we apply convolutional pooling and re-attention to enrich the
extracted features and enhance efficacy. Moreover, we employ the barely
discussed image keyframes in model training for performance improvement and
visualize the feature quantity gap between the key and normal image frames
caused by video compression. We finally illustrate the transferability with
extensive experiments on several Deepfake benchmark datasets. The proposed
solution consistently outperforms several state-of-the-art baselines on both
within- and cross-dataset experiments.
