A Survey on Face Anti-Spoofing Algorithms

The development of artificial intelligence makes the application of face recognition more and more extensive,which also leads to the security of face recognition technology increasingly prominent.How to design a face anti-spoofing method with high accuracy,strong generalization ability and meeting practical needs is the focus of current research.This paper introduces the research progress of face anti-spoofing algorithm,and divides the existing face anti-spoofing methods into two categories:methods based on manual feature expression and methods based on deep learning.Then,the typical algorithms included in them are classified twice,and the basic ideas,advantages and disadvantages of these algorithms are analyzed.Finally,the methods of face anti-spoofing are summarized,and the existing problems and future prospects are expounded.

Research on Face Anti-Spoofing Algorithm Based on Image Fusion

Along with the rapid development of biometric authentication technology,face recognition has been commercially used in many industries in recent years.However,it cannot be ignored that face recognition-based authentication techniques can be easily spoofed using various types of attacks such photographs,videos or forged 3D masks.In order to solve this problem,this work proposed a face anti-fraud algorithm based on the fusion of thermal infrared images and visible light images.The normal temperature distribution of the human face is stable and characteristic,and the important physiological information of the human body can be observed by the infrared thermal images.Therefore,based on the thermal infrared image,the pixel value of the pulse sensitive area of the human face is collected,and the human heart rate signal is detected to distinguish between real faces and spoofing faces.In order to better obtain the texture features of the face,an image fusion algorithm based on DTCWT and the improved Roberts algorithm is proposed.Firstly,DTCWT is used to decompose the thermal infrared image and visible light image of the face to obtain high-and low-frequency subbands.Then,the method based on region energy and the improved Roberts algorithm are then used to fuse the coefficients of the high-and low-frequency subbands.Finally,the DTCWT inverse transform is used to obtain the fused image containing the facial texture features.Face recognition is carried out on the fused image to realize identity authentication.Experimental results show that this algorithm can effectively resist attacks from photos,videos or masks.Compared with the use of visible light images alone for face recognition,this algorithm has higher recognition accuracy and better robustness.

A GNSS Anti-Spoofing Technique Based on the Spatial Distribution Characteristic of the Residual Vectors

Anti-spoofing is becoming a crucial technique for applications with high navigation accuracy and reliability requirements.Anti-spoofing technique based on Receiver Autonomous Integrity Monitoring(RAIM)is a good choice for most Global Navigation Satellite System(GNSS)receivers because it does not require any change to the hardware of the receiver.However,the conventional RAIM method can only detect and mitigate a single spoofing signal.Some improved RAIM methods can deal with more spoofing signals,but the computational complexity increases dramatically when the number of satellites in view increase or need additional information.This paper proposes a new RAIM method,called the SRV-RAIM method,which has a very low computation complexity regardless of the number of satellites in view and can deal with any number of spoofing signals.The key to the new method is the spatial distribution characteristic of the Satellites'Residual Vectors(SRV).In replay or generative spoofing scenarios,the pseudorange measurements of spoofing signals are consistent,the residual vectors of real satellites and those of spoofing satellites have good separation characteristics in spatial distribution.Based on this characteristic,the SRV-RAIM method is proposed,and the simulation results show that the method can separate the real signals and the spoofing signals with an average probability of 86.55%in the case of 12 visible satellites,regardless of the number of spoofing signals.Compared to the conventional traversal-RAIM method,the performance is only reduced by 3.59%,but the computational cost is reduced by 98.3%,so most of the GNSS receivers can run the SRV-RAIM algorithm in time.

Research on BeiDou anti-spoofing technology based on comprehensive radio determination satellite service

The BeiDou system(BDS)plays a significant role in people’s lives,but its security is easily affected by spoofing attacks.The radio determination satellite service(RDSS)is a special service of BDS that provides two-way communication,positioning,and timing services independently of the traditional radio navigation satellite service(RNSS).It can additionally be combined with RNSS to provide a comprehensive RDSS(CRDSS)service.In RDSS,after receiving a signal from the master station,the user needs to send a response signal back to the master station through a satellite.Therefore,the RDSS signal is difficult to spoof.In this study,based on the security feature of RDSS signals,an anti-spoofing method based on CRDSS is proposed to detect and mitigate spoofing attacks,verifying the advantages of the BeiDou system over other satellite navigation systems.

Face anti-spoofing based on multi-modal and multi-scale features fusion

Face anti-spoofing is used to assist face recognition system to judge whether the detected face is real face or fake face. In the traditional face anti-spoofing methods, features extracted by hand are used to describe the difference between living face and fraudulent face. But these handmade features do not apply to different variations in an unconstrained environment. The convolutional neural network(CNN) for face deceptions achieves considerable results. However, most existing neural network-based methods simply use neural networks to extract single-scale features from single-modal data, while ignoring multi-scale and multi-modal information. To address this problem, a novel face anti-spoofing method based on multi-modal and multi-scale features fusion(MMFF) is proposed. Specifically, first residual network(Resnet)-34 is adopted to extract features of different scales from each modality, then these features of different scales are fused by feature pyramid network(FPN), finally squeeze-and-excitation fusion(SEF) module and self-attention network(SAN) are combined to fuse features from different modalities for classification. Experiments on the CASIA-SURF dataset show that the new method based on MMFF achieves better performance compared with most existing methods.