Dependable identity recognition and authorization based on visual information

Chunlei PENG; Xinbo GAO; Nannan WANG; Jie LI

doi:10.11959/j.issn.1000-0801.2020293

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Dependable identity recognition and authorization based on visual information

Comprehensive Review | 更新时间：2024-06-05

- Dependable identity recognition and authorization based on visual information
- Telecommunications Science Vol. 36, Issue 11, Pages: 1-17(2020)
- 作者机构：
  
  1. 西安电子科技大学综合业务网理论及关键技术国家重点实验室，陕西西安 710071
  2. 重庆邮电大学重庆市图像认知重点实验室，重庆 400065
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2020293
  CLC： TP393
- Published Online：2020-11，
  
  Published：20 November 2020
- 稿件说明：
移动端阅览
Chunlei PENG, Xinbo GAO, Nannan WANG, et al. Dependable identity recognition and authorization based on visual information[J]. Telecommunications science, 2020, 36(11): 1-17.
DOI：

Chunlei PENG, Xinbo GAO, Nannan WANG, et al. Dependable identity recognition and authorization based on visual information[J]. Telecommunications science, 2020, 36(11): 1-17. DOI： 10.11959/j.issn.1000-0801.2020293.

摘要

近年来，深度学习技术在基于视频和图像等可视数据的身份识别和认证任务（如人脸、行人识别等）中得到了广泛应用。然而，机器学习（特别是深度学习模型）容易受到特定的对抗攻击干扰，从而误导身份识别系统做出错误的判断。因此，针对身份识别系统的可信认证技术研究逐渐成为当前的研究热点。分别从基于信息空间和物理空间的可视数据身份识别和认证攻击方法展开介绍，分析了针对人脸检测与识别系统、行人重识别系统的攻击技术及进展，以及基于人脸活体伪造和可打印对抗图案的物理空间攻击方法，进而讨论了可视数据身份匿名化和隐私保护技术。最后，在简要介绍现有研究中采用的数据库、实验设置与性能分析的基础上，探讨了可能的未来研究方向。

Abstract

Recently

deep learning has been widely applied to video and image based identity recognition and authorization tasks

including face recognition and person identification.However

machine learning models

especially deep learning models

can be easily fooled by adversarial attacks

which may cause the identity recognition systems to make a wrong decision.Therefore

dependable identity recognition and authorization has become one of the hot topics currently.Recent advances on dependable identity recognition and authorization from both information space and physical space were presented

where the development of the attack models on face detection

face recognition

person re-identification

and face anti-spoofing as well as printable adversarial patches were introduced.The algorithms of visual identity anonymization and privacy protection were further discussed.Finally

the datasets

experimental protocols and performance of dependable identity recognition methods were summarized

and the possible directions in the future research were presented.

关键词

Keywords

references

章坚武 , 姚泽瑾 , 吴震东 . 基于生物特征和混沌映射的多服务器身份认证方案 [J ] . 电信科学 , 2017 , 33 ( 2 ): 22 - 31 .

ZHANG J W , YAO Z J , WU Z D . Multi-server identity authen-tication scheme based on biometric and chaotic maps [J ] . Tele-communications Science , 2017 , 33 ( 2 ): 22 - 31 .

叶钰 , 王正 , 梁超 , 等 . 多源数据行人重识别研究综述 [J ] . 自动化学报 , 2019 , 46 ( 9 ): 1869 - 1884 .

YE Y , WANG Z , LIANG C , et al . A survey on multi-source person re-identification [J ] . Acta Automatica Sinica , 2019 , 46 ( 9 ): 1869 - 1884 .

NAMBIAR A , BERNARDINO A , NASCIMENTO J C . Gait-based person re-identification:a survey [J ] . ACM Computing Surveys , 2019 , 52 ( 2 ): 1 - 34 .

WANG M , DENG W . Deep face recognition:a survey [J ] . arXiv preprint arXiv:1804.06655 , 2018

唐彪 , 金炜 , 符冉迪 , 等 . 多稀疏表示分类器决策融合的人脸识别 [J ] . 电信科学 , 2018 , 34 ( 4 ): 31 - 40 .

TANG B , JIN W , FU R D , et al . Face recognition using decision fusion of multiple sparse representation-based classifiers [J ] . Telecommunications Science , 2018 , 34 ( 4 ): 31 - 40 .

SZEGEDY C , ZAREMBA W , SUTSKEVER I , et al . Intriguing properties of neural networks [J ] . arXiv preprint arXiv:1312.6199 , 2013

GOODFELLOW I J , SHLENS J , SZEGEDY C . Explaining and harnessing adversarial examples [J ] . arXiv preprint arXiv:1412.6572 , 2014

MOOSAVI-DEZFOOLI S M , FAWZI A , FROSSARD P . Deepfool:a simple and accurate method to fool deep neural networks [C ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2016 : 2574 - 2582 .

SU J , VARGAS D V , SAKURAI K . One pixel attack for fooling deep neural networks [J ] . IEEE Transactions on Evolutionary Computation , 2019 , 23 ( 5 ): 828 - 841 .

PANG T , DU C , DONG Y , et al . Towards robust detection of adversarial examples [C ] // Proceedings of Advances in Neural Information Processing Systems . Montréal:ACM Press , 2018 : 4579 - 4589 .

JIA X , WEI X , CAO X , et al . Comdefend:an efficient image compression model to defend adversarial examples [C ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2019 : 6084 - 6092 .

张思思 , 左信 , 刘建伟 . 深度学习中的对抗样本问题 [J ] . 计算机学报 , 2019 ( 8 ):15.

ZHANG S S , ZUO X , LIU J W . The problem of the adversarial examples in deep learning [J ] . Chinese Journal of Computers , 2019 ( 8 ):15.

YUAN X , HE P , ZHU Q , et al . Adversarial examples:attacks and defenses for deep learning [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2019 , 30 ( 9 ): 2805 - 2824 .

HE Y , MENG G , CHEN K , et al . Towards privacy and security of deep learning systems:a survey [J ] . arXiv preprint arXiv:1911.12562 , 2019

FINLAYSON S G , BOWERS J D , ITO J , et al . Adversarial attacks on medical machine learning [J ] . Science , 2019 , 363 ( 6433 ): 1287 - 1289 .

HEAVEN D . Why deep-learning AIs are so easy to fool [J ] . Nature , 2019 , 574 ( 7777 ):163.

沈昌祥 , 张焕国 , 王怀民 , 等 . 可信计算的研究与发展 [J ] . 中国科学:信息科学 , 2010 , 40 ( 2 ): 139 - 166 .

SHEN C X , ZHANG H G , WANG H M , et al . Research and development on trusted computation [J ] . Science China:Infor-mation Science , 2010 , 40 ( 2 ): 139 - 166 .

SHARIF M , BHAGAVATULA S , BAUER L , et al . Accessorize to a crime:real and stealthy attacks on state-of-the-art face recognition [C ] // Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security . New York:ACM Press , 2016 : 1528 - 1540 .

GOSWAMI G , RATHA N , AGARWAL A , et al . Unravelling robustness of deep learning based face recognition against adversarial attacks [C ] // Proceedings of Thirty-Second AAAI Conference on Artificial Intelligence . New Orleans:AAAI Press , 2018 .

SONG Q , WU Y , YANG L . Attacks on state-of-the-art face recognition using attentional adversarial attack generative network [J ] . arXiv preprint arXiv:1811.12026 , 2018

DEB D , ZHANG J , JAIN A K . Advfaces:adversarial face synthesis [J ] . arXiv preprint arXiv:1908.05008 , 2019

WANG R , JUEFEI-XU F , XIE X , et al . Amora:black-box adversarial morphing attack [J ] . arXiv preprint arXiv:1912.03829 , 2019

BOSE A J , AARABI P . Adversarial attacks on face detectors using neural net based constrained optimization [C ] // Proceedings of 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) . Piscataway:IEEE Press , 2018 : 1 - 6 .

GIRSHICK R , . Fast R-CNN [C ] // Proceedings of the IEEE International Conference on Computer Vision . Piscataway:IEEE Press , 2015 : 1440 - 1448 .

YANG X , WEI F , ZHANG H , et al . Design and interpretation of universal adversarial patches in face detection [J ] . arXiv preprint arXiv:1912.05021 , 2019

ROZSA A , GÜNTHER M , RUDD E M , et al . Facial attributes:accuracy and adversarial robustness [J ] . Pattern Recognition Letters , 2019 ( 124 ): 100 - 108 .

BAI S , LI Y , ZHOU Y , et al . Metric attack and defense for person re-identification [J ] . arXiv preprint arXiv:1901.10650 , 2019

DING W , WEI X , HONG X , et al . Universal adversarial perturbations against person re-identification [J ] . arXiv preprint arXiv:1910.14184 , 2019

JIA M , YANG H , HUANG D , et al . Attacking gait recognition systems via silhouette guided GANs [C ] // Proceedings of the 27th ACM International Conference on Multimedia . New York:ACM Press , 2019 : 638 - 646 .

HE Z , WANG W , DONG J , et al . Temporal sparse adversarial attack on gait recognition [J ] . arXiv preprint arXiv:2002.09674 , 2020

LU J , SIBAI H , FABRY E . Adversarial examples that fool detectors [J ] . arXiv preprint arXiv:1712.02494 , 2017

HENDRIK METZEN J , CHAITHANYA KUMAR M , BROX T , et al . Universal adversarial perturbations against semantic image segmentation [C ] // Proceedings of the IEEE International Conference on Computer Vision . Piscataway:IEEE Press , 2017 : 2755 - 2764 .

CHE Z , BORJI A , ZHAI G , et al . Adversarial attacks against deep saliency models [J ] . arXiv preprint arXiv:1904.01231 , 2019

XIAO Q , CHEN Y , SHEN C , et al . Seeing is not believing:camouflage attacks on image scaling algorithms [C ] // Proceedings of 28th USENIX Security Symposium (USENIX Security 19) . Santa Clara:USENIX Association , 2019 : 443 - 460 .

WEI X , ZHU J , YUAN S , et al . Sparse adversarial perturbations for videos [C ] // Proceedings of the AAAI Conference on Artificial Intelligence . Honolulu:AAAI Press , 2019 : 8973 - 8980 .

JIANG L , MA X , CHEN S , et al . Black-box adversarial attacks on video recognition models [C ] // Proceedings of the 27th ACM International Conference on Multimedia . New York:ACM Press , 2019 : 864 - 872 .

NAEH I , PONY R , MANNOR S . Patternless adversarial attacks on video recognition networks [J ] . arXiv preprint arXiv:2002.05123 , 2020

TANG S , HUANG X , CHEN M , et al . Adversarial attack type I:cheat classifiers by significant changes [J ] . arXiv preprint arXiv:1809.00594 , 2018

KURAKIN A , GOODFELLOW I , BENGIO S . Adversarial examples in the physical world [J ] . arXiv preprint arXiv:1607.02533 , 2016

EYKHOLT K , EVTIMOV I , FERNANDES E , et al . Robust physical-world attacks on deep learning visual classification [C ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2018 : 1625 - 1634 .

ZHANG Y , FOROOSH H , DAVID P , et al . CAMOU:learning physical vehicle camouflages to adversarially attack detectors in the wild [C ] // Proceedings of International Conference on Learning Representations.Vancouver:OpenReview . net , 2018 : 1 - 20 .

PAN G , SUN L , WU Z , et al . Eyeblink-based anti-spoofing in face recognition from a generic Web camera [C ] // Proceedings of 2007 IEEE 11th International Conference on Computer Vision . Piscataway:IEEE Press , 2007 : 1 - 8 .

LI X , KOMULAINEN J , ZHAO G , et al . Generalized face anti-spoofing by detecting pulse from face videos [C ] // Proceedings of 2016 23rd International Conference on Pattern Recognition (ICPR) . Piscataway:IEEE Press , 2016 : 4244 - 4249 .

YANG J , LEI Z , LI S Z . Learn convolutional neural network for face anti-spoofing [J ] . arXiv preprint arXiv:1408.5601 , 2014

LIU Y , JOURABLOO A , LIU X . Learning deep models for face anti-spoofing:binary or auxiliary supervision [C ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2018 : 389 - 398 .

TU X , ZHANG H , XIE M , et al . Enhance the motion cues for face anti-spoofing using CNN-LSTM architecture [J ] . arXiv preprint arXiv:1901.05635 , 2019

LIU Y , TAI Y , LI J , et al . Aurora guard:real-time face anti-spoofing via light reflection [J ] . arXiv preprint arXiv:1902.10311 , 2019

YU Z , ZHAO C , WANG Z , et al . Searching central difference convolutional networks for face anti-spoofing [J ] . arXiv preprint arXiv:2003.04092 , 2020

LI H , LI W , CAO H , et al . Unsupervised domain adaptation for face anti-spoofing [J ] . IEEE Transactions on Information Forensics and Security , 2018 , 13 ( 7 ): 1794 - 1809 .

YANG X , LUO W , BAO L , et al . Face anti-spoofing:model matters,so does data [C ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2019 : 3507 - 3516 .

LIU Y , STEHOUWER J , JOURABLOO A , et al . Deep tree learning for zero-shot face anti-spoofing [C ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2019 : 4680 - 4689 .

LIU A , TAN Z , LI X , et al . Static and dynamic fusion for multi-modal cross-ethnicity face anti-spoofing [J ] . arXiv preprint arXiv:1912.02340 , 2019

GALBALLY J , MARCEL S , FIERREZ J . Biometric antispoofing methods:a survey in face recognition [J ] . IEEE Access , 2014 ( 2 ): 1530 - 1552 .

蒋方玲 , 刘鹏程 , 周祥东 . 人脸活体检测综述 [J ] . 自动化学报 , 2020 ( 1 ): 1 - 24 .

JIANG F L , LIU P C , ZHOU X D . A review on face anti-spoofing [J ] . ACTA Automatica Sinica , 2020 ( 1 ): 1 - 24 .

SHARIF M , BHAGAVATULA S , BAUER L , et al . A general framework for adversarial examples with objectives [J ] . ACM Transactions on Privacy and Security , 2019 , 22 ( 3 ): 1 - 30 .

CHEN X , LIU C , LI B , et al . Targeted backdoor attacks on deep learning systems using data poisoning [J ] . arXiv preprint arXiv:1712.05526 , 2017

KOMKOV S , PETIUSHKO A . AdvHat:real-world adversarial attack on ArcFace face ID system [J ] . arXiv preprint arXiv:1908.08705 , 2019

THYS S , VAN RANST W , GOEDEMÉ T . Fooling automated surveillance cameras:adversarial patches to attack person detection [C ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops . Piscataway:IEEE Press , 2019 : 1 - 7 .

XU K , ZHANG G , LIU S , et al . Adversarial t-shirt! evading person detectors in a physical world [J ] . arXiv preprint arXiv:1910.11099 , 2019

WANG Z , ZHENG S , SONG M , et al . advPattern:physical-world attacks on deep person re-identification via adversarially transformable patterns [C ] // Proceedings of the IEEE International Conference on Computer Vision . Piscataway:IEEE Press , 2019 : 8341 - 8350 .

WIYATNO R R , XU A . Physical adversarial textures that fool visual object tracking [C ] // Proceedings of the IEEE International Conference on Computer Vision . Piscataway:IEEE Press , 2019 : 4822 - 4831 .

SUN Q , MA L , JOON OH S , et al . Natural and effective obfuscation by head inpainting [C ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2018 : 5050 - 5059 .

GROSS R , SWEENEY L , DE LA TORRE F , et al . Model-based face de-identification [C ] // Proceedings of 2006 Conference on Computer Vision and Pattern Recognition Workshop . Piscataway:IEEE Press , 2006 : 161 - 161 .

NEUSTAEDTER C , GREENBERG S , BOYLE M . Blur filtration fails to preserve privacy for home-based video conferencing [J ] . ACM Transactions on Computer-Human Interaction , 2006 , 13 ( 1 ): 1 - 36 .

NEWTON E M , SWEENEY L , MALIN B . Preserving privacy by de-identifying face images [J ] . IEEE Transactions on Knowledge and Data Engineering , 2005 , 17 ( 2 ): 232 - 243 .

PADILLA-LÓPEZ J R , CHAARAOUI A A , FLÓREZ- REVUELTA F . Visual privacy protection methods:a survey [J ] . Expert Systems with Applications , 2015 , 42 ( 9 ): 4177 - 4195 .

MEDEN B , EMERSIC Z , STRUC V , et al . κ-Same-Net:neural-network-based face deidentification [C ] // Proceedings of 2017 International Conference and Workshop on Bioinspired Intelligence . Piscataway:IEEE Press , 2017 : 1 - 7 .

HUKKELÅS H , MESTER R , LINDSETH F . DeepPrivacy:a generative adversarial network for face anonymization [C ] // Proceedings of International Symposium on Visual Computing . Berlin:Springer , 2019 : 565 - 578 .

WU Y , YANG F , XU Y , et al . Privacy-protective-GAN for privacy preserving face de-identification [J ] . Journal of Computer Science and Technology , 2019 , 34 ( 1 ): 47 - 60 .

GAFNI O , WOLF L , TAIGMAN Y . Live face de-identification in video [C ] // Proceedings of the IEEE International Conference on Computer Vision . Piscataway:IEEE Press , 2019 : 9378 - 9387 .

GU X , LUO W , RYOO M S , et al . Password-conditioned anonymization and deanonymization with face identity transformers [J ] . arXiv preprint arXiv:1911.11759 , 2019

OTHMAN A , ROSS A . Privacy of facial soft biometrics:suppressing gender but retaining identity [C ] // Proceedings of European Conference on Computer Vision . Berlin:Springer , 2014 : 682 - 696 .

MIRJALILI V , ROSS A . Soft biometric privacy:retaining biometric utility of face images while perturbing gender [C ] // Proceedings of 2017 IEEE International Joint Conference on Biometrics (IJCB) . Piscataway:IEEE Press , 2017 : 564 - 573 .

CHHABRA S , SINGH R , VATSA M , et al . Anonymizing k-facial attributes via adversarial perturbations [J ] . arXiv preprint arXiv:1805.09380 , 2018

MIRJALILI V , RASCHKA S , ROSS A . PrivacyNet:semi-adversarial networks for multi-attribute face privacy [J ] . arXiv preprint arXiv:2001.00561 , 2020

YI D , LEI Z , LIAO S , et al . Learning face representation from scratch [J ] . arXiv preprint arXiv:1411.7923 , 2014

GUO Y , ZHANG L , HU Y , et al . Ms-celeb-1m:a dataset and benchmark for large-scale face recognition [C ] // Proceedings of European Conference on Computer Vision . Berlin:Springer , 2016 : 87 - 102 .

HUANG G B , MATTAR M , BERG T , et al . Labeled faces in the wild:a database forstudying face recognition in unconstrained environments [Z ] . 2008 .

LIU W , WEN Y , YU Z , et al . Sphereface:deep hypersphere embedding for face recognition [C ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2017 : 212 - 220 .

DENG J , GUO J , XUE N , et al . Arcface:additive angular margin loss for deep face recognition [C ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2019 : 4690 - 4699 .

CHEN S , LIU Y , GAO X , et al . Mobilefacenets:efficient CNNs for accurate real-time face verification on mobile devices [C ] // Proceedings of Chinese Conference on Biometric Recognition . Berlin:Springer , 2018 : 428 - 438 .

SCHROFF F , KALENICHENKO D , PHILBIN J . FACENET:a unified embedding for face recognition and clustering [C ] // Proceedings of the IEEE conference on computer vision and pattern recognition . Piscataway:IEEE Press , 2015 : 815 - 823 .

LIU Z , LUO P , WANG X , et al . Deep learning face attributes in the wild [C ] // Proceedings of the IEEE International Conference on Computer Vision . Piscataway:IEEE Press , 2015 : 3730 - 3738 .

KARRAS T , AILA T , LAINE S , et al . Progressive growing of gans for improved quality,stability,and variation [J ] . arXiv preprint arXiv:1710.10196 , 2017

PARKHI O M , VEDALDI A , ZISSERMAN A . Deep face recognition [Z ] . 2015

ZHENG L , SHEN L , TIAN L , et al . Scalable person re-identification:a benchmark [C ] // Proceedings of the IEEE international Conference on Computer Vision . Piscataway:IEEE Press , 2015 : 1116 - 1124 .

ZHENG Z , ZHENG L , YANG Y . Unlabeled samples generated by GAN improve the person re-identification baseline in vitro [C ] // Proceedings of the IEEE International Conference on Computer Vision . Piscataway:IEEE Press , 2017 : 3754 - 3762 .

LI W , ZHU X , GONG S . Harmonious attention network for person re-identification [C ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2018 : 2285 - 2294 .

WANG C , ZHANG Q , HUANG C , et al . Mancs:a multi-task attentional network with curriculum sampling for person re-identification [C ] // Proceedings of the European Conference on Computer Vision . Piscataway:IEEE Press , 2018 : 365 - 381 .

LUO H , GU Y , LIAO X , et al . Bag of tricks and a strong baseline for deep person re-identification [C ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops . Piscataway:IEEE Press , 2019 : 1 - 9 .

WEI L , ZHANG S , GAO W , et al . Person transfer GAN to bridge domain gap for person re-identification [C ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2018 : 79 - 88 .

WANG L , TAN T , NING H , et al . Silhouette analysis-based gait recognition for human identification [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2003 , 25 ( 12 ): 1505 - 1518 .

ZHENG S , ZHANG J , HUANG K , et al . Robust view transformation model for gait recognition [C ] // Proceedings of 2011 18th IEEE International Conference on Image Processing . Piscataway:IEEE Press , 2011 : 2073 - 2076 .

WU Z , HUANG Y , WANG L , et al . A comprehensive study on cross-view gait based human identification with deep CNNs [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2016 , 39 ( 2 ): 209 - 226 .

CHAO H , HE Y , ZHANG J , et al . Gaitset:regarding gait as a set for cross-view gait recognition [C ] // Proceedings of the AAAI Conference on Artificial Intelligence . Honolulu:AAAI Press , 2019 : 8126 - 8133 .

AMOS B , LUDWICZUK B , HARKES J , et al . Openface:face recognition with deep neural networks [C ] // Proceedings of IEEE Winter Conference on Applications of Computer Vision . Piscataway:IEEE Press , 2016 :6.

REN S , HE K , GIRSHICK R , et al . Faster R-CNN:towards real-time object detection with region proposal networks [C ] // Proceedings of Advances in Neural Information Processing Systems . Montréal:ACM Press , 2015 : 91 - 99 .

REDMON J , FARHADI A . YOLO9000:better,faster,stronger [C ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2017 : 7263 - 7271 .

DALAL N , TRIGGS B . Histograms of oriented gradients for human detection [C ] // Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2005 : 886 - 893 .

HELD D , THRUN S , SAVARESE S . Learning to track at 100 fps with deep regression networks [C ] // Proceedings of European Conference on Computer Vision . Berlin:Springer , 2016 : 749 - 765 .

LI A , TAN Z , LI X , et al . CASIA-SURF CeFA:a benchmark for multi-modal cross-ethnicity face anti-spoofing [J ] . arXiv preprint arXiv:2003.05136 , 2020

WANG G , HAN H , SHAN S , et al . Improving cross-database face presentation attack detection via adversarial domain adaptation [C ] // Proceedings of International Conference on Biometrics (ICB) . Crete:IEEE Press , 2019 : 1 - 8 .

HENDRYCKS D , ZHAO K , BASART S , et al . Natural adversarial examples [J ] . arXiv preprint arXiv:1907.07174 , 2019

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