Survey on Generalization Methods of Face Forgery Detection

Abstract

The rapid development of deep learning technology provides powerful tools for the research of deepfake.Forged videos and images are more and more difficult for human eyes to distinguish between real and fake.Videos and images on the internet may have a huge negative impact on social life,such as financial fraud,the spread of fake news,and personal bullying.At present,the fake face detection technology based on deep learning has reached a high accuracy on multiple benchmark databases such as FaceForensics++,but the detection accuracy on cross-databases is much lower than accuracy on the source database,that is,it is difficult for many detection methods to generalize to different types of forgeries,or unknown types of forgeries,which also motivates more scholars to focus on generalization methods.The generalization research of face forgery detection focuses on methods based on deep learning.Firstly,the commonly used datasets including real-world datasets and multi-task datasets for forgery detection are discussed and compared.Secondly,it classifies and summarizes the generalization of video and image tampering detection from three aspects:data,features,and learning strategies.The data refers to data augmentation in deepfake detection.The features include single-domain features such as frequency domain features and multi-domain features.The learning strategies consist of transfer learning,multi-task learning,meta-learning,and incremental learning.And the advantages and shortcomings of three different types are analyzed.Finally,the future development direction and challenges of face tampering detection generalization are discussed.

Keywords

• face forgery detection|video image tampering|generalization|media forensics|video image classification