FaceBagNet: Bag-Of-Local-Features Model for Multi-Modal Face Anti-Spoofing

Shen, Tao; Huang, Yuyu; Tong, Zhijun

doi:10.1109/cvprw.2019.00203

Cited by 85 publications

(42 citation statements)

References 21 publications

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“…ResNet34 is chosen as the backbone and multi-scale features are fused at all residual blocks. Tao et al [30] present a multi-stream CNN architecture called FaceBagNet. In order to enhance the local detailed representation ability, patch-level images are adopted as inputs.…”

Section: Related Workmentioning

confidence: 99%

Multi-Modal Face Anti-Spoofing Based on Central Difference Networks

Qin

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from presentation attacks. Existing multi-modal FAS methods rely on stacked vanilla convolutions, which is weak in describing detailed intrinsic information from modalities and easily being ineffective when the domain shifts (e.g., cross attack and cross ethnicity). In this paper, we extend the central difference convolutional networks (CDCN) [39] to a multi-modal version, intending to capture intrinsic spoofing patterns among three modalities (RGB, depth and infrared). Meanwhile, we also give an elaborate study about single-modal based CDCN. Our approach won the first place in Track Multi-Modal as well as the second place in Track Single-Modal (RGB) of ChaLearn Face Anti-spoofing Attack Detection Challenge@CVPR2020 [20]. Our final submission obtains 1.02±0.59% and 4.84±1.79% ACER in Track Multi-Modal and Track Single-Modal (RGB), respectively. The codes are available at https://github.com/ZitongYu/CDCN.

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Section: Related Workmentioning

confidence: 99%

Multi-Modal Face Anti-Spoofing Based on Central Difference Networks

Qin

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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“…In this paper, we evaluated the results of adversarial attacks and defence based on the model proposed by Shen et al [1]. As shown in Figure 4, the model has two versions, singlemodality input and multimodality input.…”

Section: Resultsmentioning

confidence: 99%

“…However, previous research has found that facial recognition systems are easily spoofed by various face presentation attacks (PAs) [1][2][3] (as shown in Figure 1). ese attacks include print attacks, video replay attacks, and 3D mask attacks.…”

Section: Introductionmentioning

confidence: 99%

Research on Multimodality Face Antispoofing Model Based on Adversarial Attacks

Mao

Weng

Huang

et al. 2021

Security and Communication Networks

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Face antispoofing detection aims to identify whether the user’s face identity information is legal. Multimodality models generally have high accuracy. However, the existing works of face antispoofing detection have the problem of insufficient research on the safety of the model itself. Therefore, the purpose of this paper is to explore the vulnerability of existing face antispoofing models, especially multimodality models, when resisting various types of attacks. In this paper, we firstly study the resistance ability of multimodality models when they encounter white-box attacks and black-box attacks from the perspective of adversarial examples. Then, we propose a new method that combines mixed adversarial training and differentiable high-frequency suppression modules to effectively improve model safety. Experimental results show that the accuracy of the multimodality face antispoofing model is reduced from over 90% to about 10% when it is attacked by adversarial examples. But, after applying the proposed defence method, the model can still maintain more than 90% accuracy on original examples, and the accuracy of the model can reach more than 80% on attack examples.

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“…With CASIA-SURF dataset, Parkin et al [28] proposed a multi-level feature aggregation network that achieves feature fusion of different modality data at both coarse and fine levels. Also, Shen et al [29] proposed a patch-based multi-stream fusion CNN architecture to extract local-spoof discriminative information. More recently, Liu et al [26] released the CASIA-SURF Cross-ethnicity Face Anti-spoofing (CeFA) dataset, covering 3 ethnicities, 3 modalities, 1607 subjects, and 2D plus 3D attacks.…”

Section: A Face Anti-spoofingmentioning

confidence: 99%

Data-Fusion-Based Two-Stage Cascade Framework for Multimodality Face Anti-Spoofing

Liu

Wei²,

Lei

et al. 2022

IEEE Trans. Cogn. Dev. Syst.

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Existing face anti-spoofing models using deep learning for multi-modality data suffer from low generalization in the case of using variety of presentation attacks such as 2D printing and high-precision 3D face masks. One of the main reasons is that the non-linearity of multi-spectral information used to preserve the intrinsic attributes between a real and a fake face are not well extracted. To address this issue, we propose a multi-modility data based two-stage cascade framework for face anti-spoofing. The proposed framework has two advantages. Firstly, we design a two-stage cascade architecture that can selectively fuse lowlevel and high-level features from different modalities to improve feature representation. Secondly, we use multi-modality data to construct a distance-free spectral on RGB and infrared (IR) to augment the non-linearity of data. The presented data fusion strategy is different from popular fusion approaches, since it can strengthen discrimination ability of network models on physical attribute features than identity structure features under certain constraints. In addition, a multi-scale patch based weighted finetuning strategy is designed to learn each specific local face region. Experimental results show that the proposed framework achieves better performance than other state-of-the-art methods on both benchmark datasets and self-established datasets, especially on multi-material masks spoofing.

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FaceBagNet: Bag-Of-Local-Features Model for Multi-Modal Face Anti-Spoofing

Cited by 85 publications

References 21 publications

Multi-Modal Face Anti-Spoofing Based on Central Difference Networks

Multi-Modal Face Anti-Spoofing Based on Central Difference Networks

Research on Multimodality Face Antispoofing Model Based on Adversarial Attacks

Data-Fusion-Based Two-Stage Cascade Framework for Multimodality Face Anti-Spoofing

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