Learning Meta Pattern for Face Anti-Spoofing

@article{Cai2021LearningMP,
  title={Learning Meta Pattern for Face Anti-Spoofing},
  author={Rizhao Cai and Zhi Li and Renjie Wan and Haoliang Li and Yongjian Hu and Alex Chichung Kot},
  journal={IEEE Transactions on Information Forensics and Security},
  year={2021},
  volume={17},
  pages={1201-1213}
}
  • Rizhao CaiZhi Li A. Kot
  • Published 13 October 2021
  • Computer Science
  • IEEE Transactions on Information Forensics and Security
Face Anti-Spoofing (FAS) is essential to secure face recognition systems and has been extensively studied in recent years. Although deep neural networks (DNNs) for the FAS task have achieved promising results in intra-dataset experiments with similar distributions of training and testing data, the DNNs’ generalization ability is limited under the cross-domain scenarios with different distributions of training and testing data. To improve the generalization ability, recent hybrid methods have… 
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11 Citations
Background Citations
5
Methods Citations
4
Results Citations
1

11 Citations

Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues

The first joint face spoofing and forgery detection benchmark using both visual appearance and physiological rPPG cues is established and it is hoped this new benchmark will facilitate the future research of both FAS and deepfake detection communities.

Generalization of Forgery Detection With Meta Deepfake Detection Model

A deepfake detection method based on meta-learning called Meta Deepfake Detection (MDD), which 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.

Fusion Methods for Face Presentation Attack Detection

A simple feature-fusion method that integrates features extracted by using pre-trained, deep learning models with more traditional colour and texture features with the aim of improving face presentation attack detection.

Image Inpainting Detection via Enriched Attentive Pattern with Near Original Image Augmentation

This work proposes near original image augmentation that pushes the inpainted images closer to the original ones (without distortion and inpainting) as the input images, which is proved to improve the detection accuracy.

DEEP LEARNING BASED CHALLENGE RESPONSE LIVELINESS MATCHING FOR PRESENTATION ATTACK DETECTION IN FACE RECOGNITION BIOMETRIC AUTHENTICATION SYSTEMS

This work proposes a liveliness approach which solves PAD as a challenge response problem and speeds up the challenge response face matching, increases the accuracy of liveliness matching and robust against environmental distortionss.

Digital and Physical Face Attacks: Reviewing and One Step Further

This survey comprehensively survey both physical and digital face attack types and datasets, and reviews the latest and most advanced progress on existing counter- attack methodologies and highlights their current limits.

Challenge Responsive Multi Modal Biometric Authentication Resilient to Presentation Attacks

A challenge response based multi modal biometric authentication resilient to presentation attack and a hybrid deep feature which provides an addition security in recognition process to ensure authentication failure for fake samples is proposed.

M3FAS: An Accurate and Robust MultiModal Mobile Face Anti-Spoofing System

An accurate and robust face anti-spoofing system named M 3 FAS is devised, which combines visual and auditory modalities using three pervasively available sensors: camera, speaker, and microphone, and a novel two-branch neural network with three hierarchical feature aggregation modules to perform cross-modal feature fusion.

Defaek: Domain Effective Fast Adaptive Network for face anti-spoofing

Domain Generalization via Ensemble Stacking for Face Presentation Attack Detection

Synthetic data is generated by proposing a video distillation technique that blends a spatiotemporal warped image with a still image based on alpha compositing and a meta-classifier is employed to learn from experts collaboratively so that when developing an ensemble, they can leverage complementary information from each other to better tackle or be more useful for an unseen target domain.

References

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