  Face forgery by deepfake is widely spread over the internet and has raised
severe societal concerns. Recently, how to detect such forgery contents has
become a hot research topic and many deepfake detection methods have been
proposed. Most of them model deepfake detection as a vanilla binary
classification problem, i.e, first use a backbone network to extract a global
feature and then feed it into a binary classifier (real/fake). But since the
difference between the real and fake images in this task is often subtle and
local, we argue this vanilla solution is not optimal. In this paper, we instead
formulate deepfake detection as a fine-grained classification problem and
propose a new multi-attentional deepfake detection network. Specifically, it
consists of three key components: 1) multiple spatial attention heads to make
the network attend to different local parts; 2) textural feature enhancement
block to zoom in the subtle artifacts in shallow features; 3) aggregate the
low-level textural feature and high-level semantic features guided by the
attention maps. Moreover, to address the learning difficulty of this network,
we further introduce a new regional independence loss and an attention guided
data augmentation strategy. Through extensive experiments on different
datasets, we demonstrate the superiority of our method over the vanilla binary
classifier counterparts, and achieve state-of-the-art performance.
