Generalization of Forgery Detection With Meta Deepfake Detection Model

Face forgery generating algorithms that produce a range of manipulated videos/images have developed quickly. Consequently, this causes an increase in the production of fake information, making it difficult to identify. Because facial manipulation technologies raise severe concerns, face forgery detection is gaining increasing attention in the area of computer vision. In real-world applications, face forgery detection systems frequently encounter and perform poorly in unseen domains, due to poor generalization. In this paper, we propose a deepfake detection method based on meta-learning called Meta Deepfake Detection (MDD). The goal of the model is to develop a generalized model capable of directly solving new unseen domains without the need for model updates. The MDD algorithm establishes various weights for facial images from various domains. Specifically, MDD uses meta-weight learning to shift information from the source domains to the target domains with meta-optimization steps, which aims for the model to generate effective representations of the source and target domains. We build multi-domain sets using meta splitting strategy to create a meta-train set and meta-test set. Based on these sets, the model determines the gradient descent and obtains backpropagation. The inner and outer loop gradients were aggregated to update the model to enhance generalization. By introducing pair-attention loss and average-center alignment loss, the detection capabilities of the system were substantially enhanced. In addition, we used some evaluation benchmarks established from several popular deepfake datasets to compare the generalization of our proposal in several baselines and assess its effectiveness.

Abstract licence: CC BY-NC-ND

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.