DeepAI
Log In Sign Up

Multi-attentional Deepfake Detection

03/03/2021
by   Hanqing Zhao, et al.
10

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.

READ FULL TEXT

page 1

page 7

page 8

06/24/2021

Detection of Deepfake Videos Using Long Distance Attention

With the rapid progress of deepfake techniques in recent years, facial v...
12/28/2021

Exploiting Fine-grained Face Forgery Clues via Progressive Enhancement Learning

With the rapid development of facial forgery techniques, forgery detecti...
04/08/2022

On Improving Cross-dataset Generalization of Deepfake Detectors

Facial manipulation by deep fake has caused major security risks and rai...
06/21/2021

Cross-layer Navigation Convolutional Neural Network for Fine-grained Visual Classification

Fine-grained visual classification (FGVC) aims to classify sub-classes o...
03/02/2021

Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain

The remarkable success in face forgery techniques has received considera...
04/06/2022

A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning

The diffusion of rumors on microblogs generally follows a propagation tr...