Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing

Huang, Hsin-Ping; Sun, Daofeng; Liu, Yaojie; Chu, Wen-Sheng; Xiao, Taihong; Yuan, Jinwei; Adam, Hartwig; Yang, Ming–Hsuan

doi:10.48550/arxiv.2203.12175

Cited by 2 publications

(2 citation statements)

References 55 publications

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“…Tran-sRPPG [62] proposes a pure rPPG transformer framework for mining the global relationship within MSTmaps for liveness representation. ViTAF [63] uses the pure ViT to solve the zero-and few-shot, face anti-spoofing task. Contemporaneous, MA-ViT [64] aims to solve flexible modal face antispoofing by introducing Modality-Agnostic Transformer Block (MATB), which consists of two stacked attentions named Modal-Disentangle Attention (MDA) and Cross-Modal Attention (CMA), to eliminate modality-related information for each modal sequences and supplement modality-agnostic liveness features from another modal sequences, respectively.…”

Section: Related Workmentioning

confidence: 99%

CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-ethnicity Face Anti-spoofing

Liu¹,

Tan

Wan

et al. 2021

2021 IEEE Winter Conference on Applications of Computer Vision (WACV)

102

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The availability of handy multi-modal (i.e., RGB-D) sensors has brought about a surge of face anti-spoofing research. However, the current multi-modal face presentation attack detection (PAD) has two defects: (1) The framework based on multimodal fusion requires providing modalities consistent with the training input, which seriously limits the deployment scenario.(2) The performance of ConvNet-based model on high fidelity datasets is increasingly limited. In this work, we present a pure transformer-based framework, dubbed the Flexible Modal Vision Transformer (FM-ViT), for face anti-spoofing to flexibly target any single-modal (i.e., RGB) attack scenarios with the help of available multi-modal data. Specifically, FM-ViT retains a specific branch for each modality to capture different modal information and introduces the Cross-Modal Transformer Block (CMTB), which consists of two cascaded attentions named Multi-headed Mutual-Attention (MMA) and Fusion-Attention (MFA) to guide each modal branch to mine potential features from informative patch tokens, and to learn modality-agnostic liveness features by enriching the modal information of own CLS token, respectively. Experiments demonstrate that the single model trained based on FM-ViT can not only flexibly evaluate different modal samples, but also outperforms existing single-modal frameworks by a large margin, and approaches the multi-modal frameworks introduced with smaller FLOPs and model parameters.

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Section: Related Workmentioning

confidence: 99%

CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-ethnicity Face Anti-spoofing

Liu¹,

Tan

Wan

et al. 2021

2021 IEEE Winter Conference on Applications of Computer Vision (WACV)

102

View full text Add to dashboard Cite

show abstract

“…The method SSAN [40] is designed to split the representation into content and style ones with different supervision. ViTAF method [19] uses adaptive transformers as backbone and has good performance on cross-domain task. But this model requires large additional datasets for supervised pretraining.…”

Section: Introductionmentioning

confidence: 99%

Spoofing Attacks and Anti-Spoofing Methods for Face Authentication Over Smartphones

Zhang¹,

Wang²

2023

IEEE Commun. Mag.

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Face anti-spoofing (FAS) method performs well under the intra-domain setups. But cross-domain performance of the model is not satisfying. Domain generalization method has been used to align the feature from different domain extracted by convolutional neural network (CNN) backbone. However, the improvement is limited. Recently, the Vision Transformer (ViT) model has performed well on various visual tasks. But ViT model relies heavily on pre-training of large-scale dataset, which cannot be satisfied by existing FAS datasets. In this paper, taking the FAS task as an example, we propose Masked Contrastive Autoencoder (MCAE) method to solve this problem using only limited data. Meanwhile in order for a feature extractor to extract common features in live samples from different domains, we combine Masked Image Model (MIM) with supervised contrastive learning to train our model. Some intriguing design principles are summarized for performing MIM pre-training for downstream tasks. We also provide insightful analysis for our method from an information theory perspective. Experimental results show our approach has good performance on extensive public datasets and outperforms the state-ofthe-art methods.

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Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing

Cited by 2 publications

References 55 publications

CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-ethnicity Face Anti-spoofing

CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-ethnicity Face Anti-spoofing

Spoofing Attacks and Anti-Spoofing Methods for Face Authentication Over Smartphones

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