├── Benchmarks ├── README.md └── CASIA+REPLAY-ATTACK.md └── README.md /Benchmarks/README.md: -------------------------------------------------------------------------------- 1 | # Overview 2 | Here are listed different methods' performance under different scenarios (databases/protocols). 3 | 4 | # Intra dataset experiments 5 | The intra dataset experiment results can be found from [here](https://entuedu-my.sharepoint.com/:x:/g/personal/rzcai_staff_main_ntu_edu_sg/EaBdT3rbjlRJoBoRXrNBPoAB9ZvQdhNkPaA9U9YzrlMWuA?e=UE0bzW) 6 | # Cross-dataset （domain generalization）benchmarks 7 | The cross-dataset (cross-domain) experiment results can be found from [here](https://entuedu-my.sharepoint.com/:x:/g/personal/rzcai_staff_main_ntu_edu_sg/EYG2KN7EtOpEqfgZIq-5BKUBeN0URostISVMbAslUqZOsg?e=xbeqRa) 8 | 9 | 10 | 11 | -------------------------------------------------------------------------------- /Benchmarks/CASIA+REPLAY-ATTACK.md: -------------------------------------------------------------------------------- 1 | 2 | **Reesults are sorted by HTER (CASIA MFSD (FASD)->REPLAY-ATTACK)** 3 | | HTER (%) | CASIA->REPLAY-ATTACK | REPLAY-ATTACK ->CASIA | 4 | | --- | :---:|:---: | 5 | | STASN | 31.5 | 30.9 | 6 | | DeSpoofing | 28.5 | 41.1 | 7 | | DRL-FAS | 28.4 | 33.2 | 8 | | Auxiliary | 27.6 | 28.4 | 9 | | 3DPC-Net | 23.4| 25.7 | 10 | | DF | 22.4 | 30.3 | 11 | | SG-TD | 17.0 | 22.8 | 12 | | BCN-MFRM | 16.6 | 36.4 | 13 | | CDCN | 15.5 | 32.6 | 14 | | SfsNet |9.8 | 29.6 | 15 | | CDCN++ | 6.5 | 29.8 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | --- 23 | BCN-MFRM: "Face Anti-Spoofing with Human Material Perception", ECCV 2020 24 | 25 | DF (Disentanglement Framework): "Face Anti-Spoofing via Disentangled Representation Learning", ECCV 2020 26 | 27 | DRL-FAS: "DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing", IEEE T-IFS 2020 28 | 29 | SfsNet: "Leveraging Shape, Reflectance and Albedo From Shading for Face Presentation Attack Detection", IEEE T-IFS 2020 30 | 31 | SG-TD: "Deep Spatial Gradient and Temporal Depth Learning for Face Anti-Spoofing", CVPR 2020 32 | 33 | CDCN(++): "Searching central difference convolutional networks for face anti-spoofing", CVPR 2020 34 | 35 | 3DPC-Net: "3DPC-Net: 3D Point Cloud Network for Face Anti-spoofing", IJCB 2020 36 | 37 | DeSpoofing: "Face De-Spoofing: Anti-Spoofing via Noise Modeling", ECCV 2018 38 | 39 | Auxiliary: "Learning deep models for face anti-spoofing: Binary or auxiliary supervision", CVPR 2018 40 | 41 | STASN: "Face Anti-Spoofing: Model Matters, So Does Data", CVPR 2018 42 | 43 | 44 | 45 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Awesome-FAS (Face Authentication Security) 2 | 3 | A curated list of Face Authentication Security (including face anti-spoofing/face presentation attack/face liveness detection, face attack models, etc.) and related resources. This is inspired by [Awesome-deep-vision](https://github.com/kjw0612/awesome-deep-vision), [Awesome-adversarial-machine-learning](https://github.com/yenchenlin/awesome-adversarial-machine-learning), [Awesome-deep-learning-papers](https://github.com/terryum/awesome-deep-learning-papers), [Awesome-NAS](https://github.com/D-X-Y/Awesome-NAS) and [Awesome-Pruing](https://github.com/he-y/Awesome-Pruning) 4 | 5 | 6 | Please feel free to pull requests or open an issue to add papers. 7 | 8 | 9 | 10 | 11 | # Contents 12 | - [Awesome-FAS (Face Authentication Security)](#awesome-fas-face-authentication-security) 13 | - [Contents](#contents) 14 | - [Databases](#databases) 15 | - [Learning-based Methods](#learning-based-methods) 16 | - [Year 2021](##Year-2021) 17 | - [Year 2020](##Year-2020) 18 | - [Year 2019-2018](##Year-2019-2018) 19 | - [Before 2018](##Before-2018) 20 | - [System / Mobile Applications](#system--mobile-applications) 21 | - [Attack](#attack) 22 | - [Notes](#Notes) 23 | 24 | 25 | 26 | 27 | # Databases 28 | | Name | Release year | Attacks | Modalities|#subjects|#videos| 29 | |:--------|:--------:|:--------:|:--------:|:--------:|:--------:| 30 | |[Ambient-Flash](https://ieeexplore.ieee.org/document/9286821)|2021|2D attacks|VIS, additional light flashing| 31 | |[3DMA](http://www.cbsr.ia.ac.cn/users/xiangyuzhu/papers/avss193dma.pdf)|2019|3D Mask|VIS,NIR|67 genuine + 48masks|920| 32 | |[CASIA-SURF 3D HiFi Mask](https://arxiv.org/abs/2104.06148)|2020|3D Mask|VIS| 33 | |[CASIA-SURF 3D Mask](http://www.cbsr.ia.ac.cn/users/jwan/database/3DMask.pdf), [paper](http://www.cbsr.ia.ac.cn/users/jwan/papers/TPAMI2020-spoof.pdf)|2020|3D Mask|VIS| 34 | |[CUHK MMLab CelebA-Spoof](https://github.com/Davidzhangyuanhan/CelebA-Spoof)|2020|2D Print, Replay (Derived from CelbeA dataset)|VIS | 35 | |[CASIA-SURF Cross-ethnicity Face Anti-spoofing (CeFA)](https://arxiv.org/abs/2003.05136), [paper](https://openaccess.thecvf.com/content/WACV2021/papers/Liu_CASIA-SURF_CeFA_A_Benchmark_for_Multi-Modal_Cross-Ethnicity_Face_Anti-Spoofing_WACV_2021_paper.pdf)|2019|2D Print, Replay; 3D print, silica gel mask| VIS, Depth, Infrared (IR)|8|192| 36 | |[IDIAP WMCA](https://www.idiap.ch/dataset/wmca)|2019|2D Print, Replay and 3D Rigid Mask, Flexible Mask, Paper Mask attacks|VIS, depth, infrared and thermal|72|6716| 37 | |[CASIA-SURF](https://www.researchgate.net/publication/329388462_CASIA-SURF_A_Dataset_and_Benchmark_for_Large-scale_Multi-modal_Face_Anti-spoofing), [paper](https://arxiv.org/pdf/1908.10654.pdf)|2018|2D Print/Cut|VIS, Depth, Infrared (IR)|1000|21000| 38 | |[IDIAP CSMAD](https://www.idiap.ch/dataset/csmad), [paper](https://www.researchgate.net/profile/Sushil-Bhattacharjee/publication/329118426_Spoofing_Deep_Face_Recognition_with_Custom_Silicone_Masks/links/5bf68556a6fdcc3a8de9317e/Spoofing-Deep-Face-Recognition-with-Custom-Silicone-Masks.pdf)|2018|Custom Silicone Mask Attack|VIS, near-infrared (NIR), Thermal from long-wave infrared (LWIR), | 39 | |[ROSE-YOUTU](http://rose1.ntu.edu.sg/datasets/faceLivenessDetection.asp)|2018|2D Replay, Print, Paper Mask|VIS|25|4225| 40 | |~~MSU SiW-M~~（unavailable now）| 2019 | 2D Replay, Print & 3D Mask|VIS| 41 | |[MSU SiW (Spoofing in the Wild)](http://cvlab.cse.msu.edu/spoof-in-the-wild-siw-face-anti-spoofing-database.html)| 2018 |2D Replay, Print|VIS|165|4620| 42 | |[HKBU-MARs](http://rds.comp.hkbu.edu.hk/mars/)|2016|3D MASK|VIS|12|1008| 43 | |[OULU-NPU](https://sites.google.com/site/oulunpudatabase/)| 2017 |2D Replay, Print|VIS|55|5940| 44 | |[IDIAP Multispectral-Spoof Dataset](https://www.idiap.ch/dataset/msspoof)|2015|2D Print|VIS, Near-Infrared|21|4704 images| 45 | |[IDIAP 3D Mask Attack Dataset (3DMAD)](https://www.idiap.ch/dataset/3dmad), [paper](https://ieeexplore.ieee.org/document/6712688)|2013|3D Mask|VIS, Depth|17|255| 46 | |[IDIAP Replay Attack](https://www.idiap.ch/dataset/replayattack)|2012|2D Replay, Print|VIS| 47 | |[CASIA MFSD (FASD)](http://www.cbsr.ia.ac.cn/english/FASDB_Agreement/Agreement.pdf)| 2012|2D Replay, Print|VIS|50|600|| 48 | 49 | 50 | # Learning-based methods 51 | ## Year 2022 52 | | Title | Venue | Note | 53 | |:--------|:--------:|:--------:| 54 | |[Learning Meta Pattern for Face Anti-Spoofing](https://arxiv.org/pdf/2110.06753.pdf)|TIFS 2022|MetaPattern, [Code](https://github.com/RizhaoCai/MetaPattern_FAS)| 55 | |[Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues](https://arxiv.org/abs/2208.05401)|ArXiv 2022|DeepFake and FAS | 56 | |[Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing](https://arxiv.org/pdf/2203.12175.pdf)|ECCV 2022| - | 57 | |[Face Anti-Spoofing Using Transformers with Relation-Aware Mechanism](https://ieeexplore.ieee.org/document/9817442)|T-BIOM 2022|| 58 | |[Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing](https://arxiv.org/pdf/2204.12685.pdf)|Preprint 2022|Probabilistic Modeling| 59 | |[Robust Face Anti-Spoofing with Dual Probabilistic Modeling](https://arxiv.org/pdf/2203.05340.pdf)|CVPR 2022|Style transfer| 60 | |[Feature Generation and Hypothesis Verification for Reliable Face Anti-Spoofing](https://arxiv.org/pdf/2112.14894.pdf)|AAAI 2022|Hypothesis Verification?| 61 | 62 | ## Year 2021 63 | | Title | Venue | Note | 64 | |:--------|:--------:|:--------:| 65 | |[Asymmetric Modality Translation For Face Presentation Attack Detection](https://arxiv.org/abs/2110.09108)|TMM 2021| | 66 | |[Consistency Regularization for Deep Face Anti-Spoofing](https://arxiv.org/pdf/2111.12320.pdf)|ArXiv 2021| | 67 | |[Dual-Branch Meta-learning Network with Distribution Alignment for Face Anti-spoofing](https://ieeexplore.ieee.org/document/9646915/keywords#keywords)|TIFS 2021| [Code](https://github.com/taylover-pei/DBMNet-TIFS)| 68 | |[Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty Learning](https://arxiv.org/pdf/2112.00568.pdf)|ArXiv 2021|| 69 | |[Unified Detection of Digital and Physical Face Attacks](http://cvlab.cse.msu.edu/pdfs/deb_liu_jain_arxiv2021_unified.pdf)|ArXiv 2021|| 70 | |[Attention-Based Spatial-Temporal Multi-Scale Network for Face Anti-Spoofing](https://ieeexplore.ieee.org/iel7/8423754/9467110/09382387.pdf)|T-BIOM 2021|| 71 | |[Self-Domain Adaptation for Face Anti-Spoofing](https://arxiv.org/abs/2102.12129)|AAAI 2021|DA| 72 | |[Detection and Continual Learning of Novel Face Presentation Attacks](https://arxiv.org/abs/2108.12081)|ICCV 2021|Continual Learning; New attack types| 73 | |[Deep Learning for Face Anti-Spoofing: A Survey](https://arxiv.org/pdf/2106.14948.pdf)|ArXiv 2021|Survey; Under review| 74 | |[Meta-teacher for Face Anti-Spoofing](https://ieeexplore.ieee.org/document/9462562)|T-PAMI 2021|MetaTeacher| 75 | |[Dual-Cross Central Difference Network for Face Anti-Spoofing](https://arxiv.org/pdf/2105.01290.pdf)|IJCAI 2021|Based on CDCN| 76 | |[Learning One Class Representations for Face Presentation Attack Detection Using Multi-Channel Convolutional Neural Networks](https://arxiv.org/abs/2007.11457)|T-IFS 2021| Multi-modality | 77 | |[Face Anti-Spoofing via Adversarial Cross-Modality Translation](http://www.cbsr.ia.ac.cn/users/jwan/papers/TIFS2021-spoof.pdf)|T-IFS 2021| Multi-modality | 78 | |[Cross Modal Focal Loss for RGBD Face Anti-Spoofing](https://arxiv.org/abs/2103.00948#:~:text=Automatic%20methods%20for%20detecting%20presentation,in%20generalizing%20to%20unseen%20attacks.)|CVPR 2021| Multi-modality | 79 | |[Data Fusion based Two-stage Cascade Framework for Multi-Modality Face Anti-Spoofing](https://www.researchgate.net/publication/349901486_Data_Fusion_based_Two-stage_Cascade_Framework_for_Multi-Modality_Face_Anti-Spoofing)|T-DSC 2021| Multi-modality | 80 | |[Improved Detection of Face Presentation Attacks Using Image Decomposition](https://arxiv.org/pdf/2103.12201.pdf)| ArXiv 2021| Albedo | 81 | |[Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection](https://arxiv.org/abs/2104.06148)|ArXiv 2021|CASIA-SURF 3DHiFi Dataset | 82 | |[TransRPPG: Remote Photoplethysmography Transformer for 3D Mask Face Presentation Attack Detection](https://arxiv.org/abs/2104.07419)|ArXiv 2021|rPPG | 83 | |[Structure Destruction and Content Combination for Face Anti-Spoofing](https://arxiv.org/pdf/2107.10628.pdf)|IJCB 2021| | 84 | |[Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing](https://arxiv.org/pdf/2108.02667.pdf)|ACM MM 2021| | 85 | ## Year 2020 86 | | Title | Venue | Note | 87 | |:--------|:--------:|:--------:| 88 | |[Revisiting Pixel-Wise Supervision for Face Anti-Spoofing](https://www.researchgate.net/publication/346303022_Revisiting_Pixel-Wise_Supervision_for_Face_Anti-Spoofing)|T-BIOM 2020|-| 89 | |[Cross-domain face presentation attack detection via multi-domain disentangled representation learning](https://arxiv.org/abs/2004.01959)|CVPR 2020|DG, Disentangledment learning| 90 | |[Rethinking Shape From Shading for Spoofing Detection](https://ieeexplore.ieee.org/document/9286821)|T-IP 2020|-| 91 | |[Camera Invariant Feature Learning for Generalized Face Anti-spoofing](https://arxiv.org/pdf/2101.10075.pdf)|T-IFS 2020| Domain generalization | 92 | |[NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing](https://arxiv.org/abs/2011.02062)| T-PAMI 2020|NAS; Domain generalization;| 93 | |[3DPC-Net: 3D Point Cloud Network for Face Anti-spoofing](http://www.cbsr.ia.ac.cn/users/jwan/papers/IJB2020-3DPCNet.pdf)|IJCB 2020|Pseudo Point Cloud| 94 | |[DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing](https://arxiv.org/pdf/2009.07529.pdf)| T-IFS 2020|Reinforcement learning| 95 | |[Face spoofing detection based on local ternary label supervision in fully convolutional networks](https://ieeexplore.ieee.org/document/9056824)| T-IFS 2020|Using a map of Ones is the same as a depth map!| 96 | |[Face Anti-Spoofing via Disentangled Representation Learning](https://arxiv.org/pdf/2008.08250.pdf)|ECCV 2020|Disentanglement learning| 97 | |[On Disentangling Spoof Trace for Generic Face Anti-Spoofing](https://arxiv.org/pdf/2007.09273.pdf)|ECCV 2020|Similar idea as the DeSpoofing method| 98 | |[CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations](https://github.com/Davidzhangyuanhan/CelebA-Spoof)|ECCV 2020|Contribute a dataset| 99 | |[Face Anti-Spoofing with Human Material Perception](https://www.researchgate.net/publication/342733508_Face_Anti-Spoofing_with_Human_Material_Perception)|ECCV 2020|2D Attacks| 100 | |[Leveraging Shape, Reflectance and Albedo from Shading for Face Presentation Attack Detection](https://www.researchgate.net/publication/340671825_Leveraging_Shape_Reflectance_and_Albedo_from_Shading_for_Face_Presentation_Attack_Detection)| T-IFS 2020|SfS for albedo, reflectance, and depth map| 101 | |[Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing](https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Deep_Spatial_Gradient_and_Temporal_Depth_Learning_for_Face_Anti-Spoofing_CVPR_2020_paper.pdf)|CVPR 2020|RGB, Depth-contrastive loss, [Code](https://github.com/clks-wzz/FAS-SGTD)| 102 | |[Searching Central Difference Convolutional Networks for Face Anti-Spoofing](https://openaccess.thecvf.com/content_CVPR_2020/papers/Yu_Searching_Central_Difference_Convolutional_Networks_for_Face_Anti-Spoofing_CVPR_2020_paper.pdf)|CVPR 2020|RGB, NAS, CDCN, [Code](https://github.com/ZitongYu/CDCN)| 103 | |[Regularized Fine-grained Meta Face Anti-spoofing](https://arxiv.org/abs/1911.10771)|AAAI 2020|RGB, 2D Attack, Meta Learning, [Code](https://github.com/rshaojimmy/AAAI2020-RFMetaFAS); Meta learning| 104 | |[Unsupervised Adversarial Domain Adaptation for Cross-Domain Face Presentation Attack Detection](https://www.researchgate.net/publication/342185474_Unsupervised_Adversarial_Domain_Adaptation_for_Cross-Domain_Face_Presentation_Attack_Detection)|T-IFS 2020|Domain adaptation| 105 | |[Face Anti-Spoofing with Deep Neural Network Distillation](https://www.researchgate.net/publication/342115009_Face_Anti-Spoofing_with_Deep_Neural_Network_Distillation)|IEEE Journal of Selected Topics in Signal Processing 2020|Domain knowledge distillation| 106 | |[Single-Side Domain Generalization for Face Anti-Spoofing](https://openaccess.thecvf.com/content_CVPR_2020/papers/Jia_Single-Side_Domain_Generalization_for_Face_Anti-Spoofing_CVPR_2020_paper.pdf)|CVPR 2020|RGB, [Code](https://github.com/taylover-pei/SSDG-CVPR2020)| 107 | |[Domain Agnostic Feature Learning for Image and Video Based Face Anti-Spoofing](https://arxiv.org/pdf/1912.07124.pdf)|CVPRW 2020|RGB, 2D Attack| 108 | |[Learning Generalized Spoof Cues for Face Anti-spoofing](https://arxiv.org/abs/2005.03922)|ArXiv 2020| RGB, 2D Attack, [Code](https://github.com/vis-var/lgsc-for-fas);| 109 | |[Learning Meta Model for Zero- and Few-shot Face Anti-spoofing](https://arxiv.org/abs/1904.12490)|AAAI 2020|RGB, 2D Presentation Attack, Meta Learning, [Code](https://github.com/qyxqyx/AIM_FAS); few-shot| 110 | |[Learning to Learn Face-PAD: a lifelong learning approach](https://www.researchgate.net/publication/348324514_Learning_to_Learn_Face-PAD_a_lifelong_learning_approach)|IJCB 2020|Life-long learning| 111 | |[Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing](https://dl.acm.org/doi/abs/10.1145/3402446)|ACM TIST 2020|DA| 112 | 113 | 114 | ## Year 2019-2018 115 | | Title | Venue | Note | 116 | |:--------|:--------:|:--------:| 117 | |[Attention-Based Two-Stream Convolutional Networks for Face Spoofing Detection](https://ieeexplore.ieee.org/document/8737949)| T-IFS 2019|RGB, 2D Attack| 118 | |[Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Network](http://publications.idiap.ch/downloads/papers/2019/George_TIFS_2019.pdf)|T-IFS 2019|RGB+IR+Depth, 2D Presentation Attack| 119 | |[Face Anti-Spoofing: Model Matters, So Does Data](http://www.cbsr.ia.ac.cn/users/jwan/papers/CVPR2019-spoofing.pdf)|CVPR 2019|RGB, 2D Attack,data augmentation| 120 | |[A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing](https://yuan-gao.net/pdf/CVPR2019%20-%20antispoofing.pdf)|CVPR 2019|RGB+IR+Depth, 2D Attack| 121 | |[Deep Tree Learning for Zero-shot Face Anti-Spoofing](http://cvlab.cse.msu.edu/pdfs/Liu_Stehouwer_Jourabloo_Liu_CVPR2019.pdf)|CVPR 2019|RGB, 2D Attack| 122 | |[Deep Anomaly Detection for Generalized Face Anti Spoofing](http://openaccess.thecvf.com/content_CVPRW_2019/papers/CFS/Perez-Cabo_Deep_Anomaly_Detection_for_Generalized_Face_Anti-Spoofing_CVPRW_2019_paper.pdf)|CVPRW 2019|RGB, 2D Attack| 123 | |[Joint Discriminative Learning of Deep Dynamic Textures for 3D Mask Face Anti-Spoofing](https://ieeexplore.ieee.org.remotexs.ntu.edu.sg/document/8453011)|T-IFS 2019|VIS, 3D Attack| 124 | |[Multi-adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection](http://openaccess.thecvf.com/content_CVPR_2019/papers/Shao_Multi-Adversarial_Discriminative_Deep_Domain_Generalization_for_Face_Presentation_Attack_Detection_CVPR_2019_paper.pdf)|CVPR 2019|RGB, 2D Attack,| 125 | |[Exploiting temporal and depth information for multi-frame face anti-spoofing](https://arxiv.org/abs/1811.05118)|ArXiv 2018|RGB, 2D Presentation Attack, Multi-frame | 126 | |[Face De-Spoofing: Anti-Spoofing via Noise Modeling](https://arxiv.org/abs/1807.09968)|ECCV 2018|RGB, 2D Attack, pseudo depth, [Code](https://github.com/yaojieliu/ECCV2018-FaceDeSpoofing)| 127 | |[Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision](http://cvlab.cse.msu.edu/pdfs/Liu_Jourabloo_Liu_CVPR2018.pdf)|CVPR 2018|RGB, 2D Attack, pseudo depth+rPPG| 128 | |[Learning generalized deep feature representation for face anti-spoofing](https://rose.ntu.edu.sg/Publications/Documents/Face%20Spoofing%20Detection/Learning%20Generalized%20Deep%20Feature%20Representation%20for%20Face%20Anti-Spoofing.pdf)| T-IFS 2018|RGB, 2D Presentation Attack,| 129 | |[Unsupervised domain adaptation for face anti-spoofing](https://ieeexplore.ieee.org/document/8279564)| T-IFS 2018|RGB, 2D Attack, unsupervised domain adaptation| 130 | 131 | 132 | ## Before 2018 133 | | Title | Venue | Note| 134 | |:--------|:--------:|:--------:| 135 | |[Face Anti-Spoofing Using Patch and Depth-Based CNNs](http://cvlab.cse.msu.edu/pdfs/FaceAntiSpoofingUsingPatchandDepthBasedCNNs.pdf)|IJCB 2017|RGB, 2D Attack, pseudo depth| 136 | |[Face Spoofing Detection Using Colour Texture Analysis](https://www.researchgate.net/publication/301571761_Face_Spoofing_Detection_Using_Colour_Texture_Analysis)| T-IFS 2016|RGB, 2D Attack, Color LBP| 137 | |[Spoofing Face Recognition With 3D Masks](https://www.researchgate.net/publication/262605045_Spoofing_Face_Recognition_With_3D_Masks)| T-IFS 2014|3D Mask| 138 | |[Face Spoofing Detection Through Visual Codebooks of Spectral Temporal Cubes](https://www.researchgate.net/publication/281054869_Face_Spoofing_Detection_Through_Visual_Codebooks_of_Spectral_Temporal_Cubes)|IEEE TIP 2015|RGB, 2D Attack,| 139 | |[Person-Specific Face Anti-Spoofing With Subject Domain Adaptation](https://ieeexplore.ieee.org/document/7041231)| T-IFS 2015|RGB, 2D Attack, Person-specific| 140 | |[Face Spoof Detection with Image Distortion Analysis](http://vipl.ict.ac.cn/uploadfile/upload/2017020711092984.pdf)| T-IFS 2015|RGB, 2D Attack, IDA| 141 | |[Using Visual Rhythms for Detecting Video-Based Facial Spoof Attacks](https://www.researchgate.net/publication/275102589_Using_Visual_Rhythms_for_Detecting_Video-Based_Facial_Spoof_Attacks)|T-IFS 2015|VIS, 2D Replay| 142 | |[Face spoofing detection from single images using texture and local shape analysis](https://ieeexplore.ieee.org/document/6117510)|IJCB 2011|RGB, 2D Presentation Attack, LBP| 143 | 144 | 145 | 146 | 147 | # System / Mobile Applications 148 | | Title | Venue | Note | 149 | |:--------|:--------:|:--------:| 150 | |[Efficient Face Spoofing Detection With Flash](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9419913)|T-BIOM|Flash| 151 | |[RFace: Anti-Spoofing Facial Authentication Using COTS RFID](https://www4.comp.polyu.edu.hk/~csyqzheng/papers/RFace-INFOCOM21.pdf)|INFOCOM 2021|RFID; privacy preserved| 152 | |[FaceRevelio: A Face Liveness Detection System for Smartphones with a Single Front Camera](https://habiba-farrukh.github.io/files/FaceRevelio.pdf)|MobiCom 2020|RGB+Flashing| 153 | |[EchoPrint: Two-factor Authentication using Acoustics and Vision on Smartphones](https://www.researchgate.net/publication/328325417_EchoPrint_Two-factor_Authentication_using_Acoustics_and_Vision_on_Smartphones)|MobiCom 2018|Acoustic+Vision| 154 | |[Face Flashing: a Secure Liveness Detection Protocol based on Light Reflections](https://arxiv.org/pdf/1801.01949.pdf)|NDSS 2018|RGB+Flashing| 155 | |[Light Field-Based Face Presentation Attack Detection: Reviewing, Benchmarking and One Step Further](https://ieeexplore.ieee.org/document/8271987)| T-IFS 2018|Light Filed-Based| 156 | |[rtCaptcha: A Real-Time CAPTCHA Based Liveness Detection System](https://pdfs.semanticscholar.org/1fb3/99bf4122b5b25ae7784ca73f9b1be6a91cde.pdf)|NDSS 2018|RGB, Captcha| 157 | |[Face Liveness Detection Using a Flash Against 2D Spoofing Attack](http://www.hebmlc.org/UploadFiles/20171014235219706.pdf)| T-IFS 2017|RGB+Flashing, 2D Attack| 158 | |[Your face your heart: Secure mobile face authentication with photoplethysmograms](https://web.asu.edu/sites/default/files/cnsg/files/c38.pdf)|INFORCOM 2017|RGB, rPPG| 159 | |[Seeing your face is not enough: An inertial sensor-based liveness detection for face authentication](https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=3884&context=sis_research)|ACM CCS 2015|RGB+Accelerometer| 160 | |[Sensor-assisted facial recognition: an enhanced biometric authentication system for smartphones](https://dl.acm.org/doi/10.1145/2594368.2594373)|Mobisys 2014|RGB+ Sensors| 161 | 162 | 163 | # Attack 164 | | Title | Venue | Note | 165 | |:--------|:--------:|:--------:| 166 | |[Light Can Hack Your Face! Black-box Backdoor Attack on Face Recognition Systems](https://arxiv.org/abs/2009.06996)|ArXiv 2020|Face Recognition Attack| 167 | |[Virtual U: Defeating Face Liveness Detection by Building Virtual Models from Your Public Photos](https://www.usenix.org/system/files/conference/usenixsecurity16/sec16_paper_xu.pdf)|USENIX SS 2018|VR tech for spoofing| 168 | 169 | 170 | # Notes 171 | + T-PAMI: [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34) 172 | + T-IFS: [IEEE Transactions on Information Forensics and Security](https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=10206) 173 | + T-IP: [IEEE Transactions on Image Processing](https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=83) 174 | + T-DSC: [IEEE Transactions on Cognitive and Developmental Systems](https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7274989) 175 | + T-BIOM [IEEE Transactions on Biometrics, Behavior, and Identity Science](https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8423754) 176 | + CVPR: [Conference on Computer Vision and Pattern Recognition](https://ieeexplore.ieee.org/xpl/conhome/1000147/all-proceedings) 177 | + ICCV: [International Conference on Computer Vision](https://ieeexplore.ieee.org/xpl/conhome/1000149/all-proceedings) 178 | + ECCV: [European Conference on Computer Vision (Springer)](https://link.springer.com/conference/eccv) 179 | + USENIX SS: Proceedings of USENIX Security Symposium 180 | + NDSS: The Network and Distributed System Security Symposium 181 | + Infocom: IEEE International Conference on Computer Communications 182 | + MobiCom: International Conference On Mobile Computing And Networking 183 | + CCS: ACM Computer and Communications Security Conference 184 | + MobiSys: International Conference on Mobile Systems, Applications, and Services 185 | + IJCB: International Joint Conference on Biometrics 186 | + ArXiv: Papers that only appear in ArXiv are regarded not published, but they are collected here for reference only. 187 | 188 | 189 | --- 190 | If you feel useful about this repo, we would be happy you are also interested in our works and use our papers your work's reference. 191 | 192 | @article{cai2020drlfas, 193 | title={DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing}, 194 | author={Cai, Rizhao and Li, Haoliang and Wang, Shiqi and Chen, Changsheng and Kot, Alex C}, 195 | journal={IEEE Transactions on Information Forensics and Security}, 196 | volume={16}, 197 | pages={937--951}, 198 | year={2020}, 199 | publisher={IEEE}, 200 | doi={10.1109/TIFS.2020.3026553} 201 | 202 | @ARTICLE{cai2022MP, 203 | author={Cai, Rizhao and Li, Zhi and Wan, Renjie and Li, Haoliang and Hu, Yongjian and Kot, Alex C.}, 204 | journal={IEEE Transactions on Information Forensics and Security}, 205 | title={Learning Meta Pattern for Face Anti-Spoofing}, 206 | year={2022}, 207 | volume={}, 208 | number={}, 209 | pages={1-1}, 210 | doi={10.1109/TIFS.2022.3158551}} 211 | } 212 | 213 | 214 | @article{li2022one, 215 | title={One-Class Knowledge Distillation for Face Presentation Attack Detection}, 216 | author={Li, Zhi and Cai, Rizhao and Li, Haoliang and Lam, Kwok-Yan and Hu, Yongjian and Kot, Alex C}, 217 | journal={IEEE Transactions on Information Forensics and Security}, 218 | year={2022}, 219 | publisher={IEEE} 220 | } 221 | 222 | 223 | --------------------------------------------------------------------------------