-
[pdf]
[bibtex]@InProceedings{Yu_2024_CVPR, author = {Yu, Jiaruo and Lu, Dagong and Shi, Xingyue and Qu, Chenfan and Guo, Fengjun}, title = {Unified Face Attack Detection with Micro Disturbance and a Two-Stage Training Strategy}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {960-969} }
Unified Face Attack Detection with Micro Disturbance and a Two-Stage Training Strategy
Abstract
Face recognition systems are widely used in real-world scenarios but are susceptible to physical and digital attacks. Effective methods for unified detection of both physical face attacks and digital face attacks are essential to ensure the reliability of face recognition systems. However how to obtain a unified face attack detection model that has adequate ability of fine-grained perception and cross-domain generalization ability remains an open challenge. To address this issue we first propose a two-stage training strategy which utilizes unlabeled face images with masked image modeling and unleashes the potential of vision transformers. Furthermore we propose a novel method termed as Micro Disturbance which successfully enriches the representation distribution of forged faces and increases the diversity of the training data thereby addressing the issue of cross-domain generalization. Attribute to the effectiveness of our proposed methods our model finally wins the third place in the 5th Face Anti-Spoofing Challenge@CVPR2024 with an impressive ACER score of 5.511.
Related Material