DeepfakeMAE: Facial Part Consistency Aware Masked Autoencoder for Deepfake Video Detection

Deepfake techniques have been used maliciously, resulting in strong research interests in developing Deepfake detection methods. Deepfake often manipulates the video content by tampering with some facial parts. However, this manipulation usually breaks the consistency among facial parts, e.g., Deepfake may change smiling lips to upset, but the eyes are still smiling. Existing works propose to spot inconsistency on some specific facial parts (e.g., lips), but they may perform poorly if new Deepfake techniques focus on the specific facial parts used by the detector. Thus, this paper proposes a new Deepfake detection model, DeepfakeMAE, which can utilize the consistencies among all facial parts. Specifically, given a real face image, we first pretrain a masked autoencoder to learn facial part consistency by randomly masking some facial parts and reconstructing missing areas based on the remaining facial parts. Furthermore, to maximize the discrepancy between real and fake videos, we propose a novel model with dual networks that utilize the pretrained encoder and decoder, respectively. 1) The pretrained encoder is finetuned for capturing the overall information of the given video. 2) The pretrained decoder is utilized for distinguishing real and fake videos based on the motivation that DeepfakeMAE's reconstruction should be more similar to a real face image than a fake one. Our extensive experiments on standard benchmarks demonstrate that DeepfakeMAE is highly effective and especially outperforms the previous state-of-the-art method by 3.1% AUC on average in cross-dataset detection.