Anti-Spoofing:Integrated Information Authentication of BeiDou-ⅡCivil Navigation Message

The BeiDou-Ⅱcivil navigation message(BDⅡ-CNAV)is transmitted in an open environment and no information integrity protection measures are provided.Hence,the BDⅡ-CNAV faces the threat of spoofing attacks,which can lead to wrong location reports and time indication.In order to deal with this threat,we proposed a scheme of anti-spoofing for BDⅡ-CNAV based on integrated information authentication.This scheme generates two type authentication information,one is authentication code information(ACI),which is applied to confirm the authenticity and reliability of satellite time information,and the other is signature information,which is used to authenticate the integrity of satellite location information and other information.Both authentication information is designed to embed into the reserved bits in BDⅡ-CNAV without changing the frame structure.In order to avoid authentication failure caused by public key error or key error,the key or public key prompt information(KPKPI)are designed to remind the receiver to update both keys in time.Experimental results indicate that the scheme can successfully detect spoofing attacks,and the authentication delay is less than 1%of the transmission delay,which meets the requirements of BDⅡ-CNAV information authentication.

Face anti-spoofing algorithm combined with CNN and brightness equalization

Face anti-spoofing is a relatively important part of the face recognition system,which has great significance for financial payment and access control systems.Aiming at the problems of unstable face alignment,complex lighting,and complex structure of face anti-spoofing detection network,a novel method is presented using a combination of convolutional neural network and brightness equalization.Firstly,multi-task convolutional neural network(MTCNN)based on the cascade of three convolutional neural networks(CNNs),P-net,R-net,and O-net are used to achieve accurate positioning of the face,and the detected face bounding box is cropped by a specified multiple,then brightness equalization is adopted to perform brightness compensation on different brightness areas of the face image.Finally,data features are extracted and classification is given by utilizing a 12-layer convolution neural network.Experiments of the proposed algorithm were carried out on CASIA-FASD.The results show that the classification accuracy is relatively high,and the half total error rate(HTER)reaches 1.02%.

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.

Face Anti-Spoofing with Unknown Attacks:A Comprehensive Feature Extraction and Representation Perspective

Face anti-spoofing aims at detecting whether the input is a real photo of a user(living)or a fake(spoofing)image.As new types of attacks keep emerging,the detection of unknown attacks,known as Zero-Shot Face Anti-Spoofing(ZSFA),has become increasingly important in both academia and industry.Existing ZSFA methods mainly focus on extracting discriminative features between spoofing and living faces.However,the nature of the spoofing faces is to trick anti-spoofing systems by mimicking the livings,therefore the deceptive features between the known attacks and the livings,which have been ignored by existing ZSFA methods,are essential to comprehensively represent the livings.Therefore,existing ZSFA models are incapable of learning the complete representations of living faces and thus fall short of effectively detecting newly emerged attacks.To tackle this problem,we propose an innovative method that effectively captures both the deceptive and discriminative features distinguishing between genuine and spoofing faces.Our method consists of two main components:a two-against-all training strategy and a semantic autoencoder.The two-against-all training strategy is employed to separate deceptive and discriminative features.To address the subsequent invalidation issue of categorical functions and the dominance disequilibrium issue among different dimensions of features after importing deceptive features,we introduce a modified semantic autoencoder.This autoencoder is designed to map all extracted features to a semantic space,thereby achieving a balance in the dominance of each feature dimension.We combine our method with the feature extraction model ResNet50,and experimental results show that the trained ResNet50 model simultaneously achieves a feasible detection of unknown attacks and comparably accurate detection of known spoofing.Experimental results confirm the superiority and effectiveness of our proposed method in identifying the living with the interference of both known and unknown spoofing types.

卫星导航接收机欺骗攻击及防护综述

本文主要研究卫星导航欺骗干扰技术与欺骗防护技术,首先介绍欺骗干扰的生成方式、欺骗干扰实施策略与播发策略,并从欺骗威胁、实现难度、防护难度等多方面给出现有欺骗技术的对比分析。其次从信号体制、终端处理技术、辅助信息校验技术等三个层面介绍现有欺骗防护技术,并从欺骗适应性、实现需求、实现难度、实现效果等几方面给出防护技术对比分析。在此基础上,研究在面对不同欺骗攻击时接收机的欺骗防护框架,对于评估导航接收机的防护能力有借鉴意义。

基于微小运动的人脸活体检测算法

针对人脸识别过程中容易受到打印攻击、视频重放攻击的问题,提出一种利用微小运动的人脸活体检测算法。该算法对视频进行运动放大处理以增强微小运动;使用运动强度和运动方向描述微小运动,生成两种运动特征图并进行融合;使用基于注意力机制的VGG16网络进行真伪判别。在Replay-attack数据集上半错误率(HTER)为1.35%,在CASIA FASD数据集上等错误率(EER)为2.5%,证明了微小运动对人脸防伪的有效性。

从信息安全角度浅析RDSS系统的安全性

针对卫星无线电测定业务(radio determination satellite service,RDSS)系统的信号结构和运行机制,分析了RDSS系统的安全优势,从信息安全的保密性、完整性和可用性三个角度研究了RDSS系统可能存在的安全隐患和应对措施,特别是采用射频录制和回放(record and replay,RAR)手段对RDSS系统可能存在的仿冒与欺骗安全隐患做了验证.证明RDSS系统在抗RAR攻击方面存在漏洞,容易被RAR攻击欺骗,这种欺骗攻击暴露了RDSS系统存在完整性和可用性上的安全隐患,可能使得终端用户造成信息混乱,也可能使终端用户被触发频次限制,无法得到正常的服务.最后提出了针对此类安全隐患提高系统安全性的几种措施.

具有对抗鲁棒性的人脸活体检测方法

现有人脸活体检测方法在深度神经网络的支持下已获得优秀的检测能力,但面临对抗样本攻击时仍呈现脆弱性。针对此问题,引入胶囊网络(Capsule Network,CapsNet)提出一种具有对抗鲁棒性的人脸活体检测方法FAS-CapsNet:通过CapsNet及其图像重建机制保留特征间关联,过滤样本中的对抗扰动;根据皮肤与平面介质的反射性质差异,以Retinex算法增强图像光照特征,增大活体与非活体人脸类间距离的同时破坏对抗扰动模式,进而提升模型准确性与鲁棒性。在CASIA-SURF数据集上进行实验可知:FAS-CapsNet对正负样本的检测准确率为87.344%,对比模型中最高准确率为78.917%,说明FAS-CapsNet具备充分的常规活体检测能力。为进一步验证模型鲁棒性,基于CASIA-SURF测试集生成两种对抗样本数据集并进行实验:FAS-CapsNet在两数据集上的检测准确率分别为84.552%和79.042%,较常规检测准确率下降3.197%和9.505%;对比模型在两数据集上的最高准确率分别为74.938%和41.667%,较常规检测下降5.042%和47.201%。可见FAS-CapsNet受对抗扰动影响更小,具有显著的对抗鲁棒性优势。

无人机协同定位防欺骗的WGAN-TM识别方法

针对无人机协同定位面临的欺骗干扰状态难以精准识别的问题,提出了一种结合WGAN和信任模型(TM)的欺骗定量识别方法。基于WGAN实现编队受扰状态的快速检测,在此基础上利用编队协同定位信任模型,实现被欺骗无人机精准识别。仿真结果表明,与同类方法比较,所提方法在甄别被欺骗无人机方面,具有较高的精确性和稳定性,并且,在训练阶段无需大量异常样本,在精准甄别阶段也无需新增大量数据交互。

利用部分可信信号的导航终端欺骗干扰检测方法

导航信号认证服务处于初步部署阶段,认证信号对地覆盖重数无法满足独立定位授时需求,现有研究对这一阶段利用部分通过认证的信号,即可信信号,实现欺骗检测的方法关注度较低。针对这一现状,该文根据欺骗攻击原理,提出以可信信号为基准,基于可信信号伪距残差的欺骗检测方法,建立该场景下的欺骗检测模型,并分析影响所提方法检测性能的因素。经过仿真,在可信卫星数目为3颗、用户定位精度约10 m条件下,当欺骗导致的定位偏差为100 m时,该方法的平均欺骗检测概率可达0.96。此外,该文对算法欺骗检测盲区进行了分析,证明所提算法对于绝大部分欺骗导致的定位结果均有效。

融合注意力机制的ResNeXt语音欺骗检测模型

针对残差神经网络在语音欺骗检测中存在超参数过多且对于高频特征显著性突出不够的问题,提出一种融合注意力机制的ResNeXt-Attention网络(RA-Net)。RA-Net采用残差结合分组卷积的方式,用一组小卷积核代替大卷积核,且采用MFM(Max Feature Map)作为新的拼接方法。加入的注意力机制通过学习原始特征的信息,减少了对边缘信息的关注。在ASVspoof2019数据集上实验表明,RA-Net相比基准线高斯混合模型(GMM)的等错误率(EER)降低了4.72百分点和6.23百分点,与残差网络(Residal Neural Network,ResNet)相比EER降低了0.69百分点和0.89百分点,证明了该模型的有效性。

注意力感知特征提取和融合的多模态人脸防伪检测方法

针对多模态人脸防伪检测中如何有效融合多模态信息的问题,提出一种注意力感知特征提取和融合的多模态人脸防伪检测方法(attention-aware feature extraction and fusion,AFEF)。在跨模态特征融合部分利用通道和空间注意力机制探索不同模态之间的互补信息,以弥补单一模态特征表达不足的问题;利用卷积融合方式融合多模态特征,以避免信息覆盖或者无关信息强化的问题;在特征提取部分引入CBAM注意力机制,获得更细粒度的各模态特征表示,便于后续进行跨模态特征融合。实验结果表明,与当前其他主流多模态人脸防伪算法相比,提出的方法在CASIA-SURF和CeFA两个多模态数据集上的平均分类错误率(average classification error rate,ACER)均最低,算法有效。

改进型MobileNetV3轻量级人脸活体检测算法

为了解决人脸识别系统中的欺诈攻击问题,以及目前基于深度学习的活体检测方法大多以大型卷积网络作为主干网络,导致模型结构复杂、计算量大等问题,提出一种改进型MobileNetV3的轻量级人脸活体检测算法。对MobileNetV3中利用全局平均池化计算通道注意力权重和使用双非线性全连接层存在的不足进行讨论,提出新的注意力机制EFCANet,并利用EFCANet网络对MobileNetV3轻量级卷积神经网络进行改进。实验结果表明,改进后轻量型活体检测算法,在检测精度、网络模型大小、损失值和等错误率等方面有着不错的表现。

牵引式欺骗对矢量跟踪环路的影响

牵引式欺骗能够在不引起跟踪环路失锁的条件下诱使接收机跟踪欺骗信号,是一种隐蔽性很高的欺骗干扰方式。对于标量接收机,由于其跟踪环路相互独立,因此针对单个信号进行牵引式欺骗时,不会受其他信号的影响。而矢量接收机的跟踪环路通过接收机状态耦合,存在相互影响,即牵引式欺骗对矢量和标量跟踪环路的影响存在差异。本文基于无噪声且欺骗信号与真实信号载波频率和载波相位相等的假设条件,分析牵引式欺骗对矢量跟踪环路的影响,推导出欺骗成功条件,并利用信号源模拟器和软件接收机对分析结果进行验证。研究结果表明,对矢量跟踪环路成功实施牵引式欺骗的条件较标量跟踪环路更为严苛,反映出矢量跟踪环路固有的抗欺骗干扰能力。

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.

基于解纠缠表示学习的人脸反欺骗算法

针对现有人脸反欺骗模型面对不同应用场景识别精度低、泛化性能不佳的问题,引入解纠缠表示学习,提出一种基于解纠缠表示学习的人脸反欺骗方法。该方法采用U-Net架构和ResNet-18作为编/解码器。首阶段训练中,通过输入真实样本使得编码器仅学习到真实样本相关信息。第二阶段,构建对抗性学习网络,输入不具标签的样本,将预训练的编码器输出和新编码器输出进行特征融合,由解码器重建图像,在鉴别器中与原始图像进行对抗训练,以实现特征的解耦。模型与一些经典人脸反欺骗方法相比,有着更好的检测性能,在OULU-NPU数据集的数个实验中,最低的检测错误率仅为0.8%,表现优于STDN等经典检测方法。该人脸反欺骗方法通过分阶段训练的方式,使得模型在对抗性训练中获得了相比端到端模型更具判别性的特征表示,在欺骗特征图输出阶段采用多分类策略,减小了不同的图像噪声对分类结果的影响,在公开数据集上的实验验证了算法的有效性。

基于内容风格增强和特征嵌入优化的人脸活体检测方法

针对现有人脸活体检测算法的特征表示不佳,以及在跨数据集上泛化性能较差等问题,提出了一种基于内容风格增强和特征嵌入优化的人脸活体检测方法。首先,使用ResNet-18编码器提取来自多个源域的通用特征,并经过不同注意力机制的两个自适应模块进行分离,增强全局内容特征与局部风格特征表征;其次,基于AdaIN算法将内容特征与风格特征进行有机融合,进一步提升特征表示,并将融合后的特征输入到特定的分类器和域判别器进行对抗训练;最后,采用平均负样本的半难样本三元组挖掘优化特征嵌入,可以兼顾类内聚集和类间排斥,更好地捕捉真实和伪造类别之间的界限。该方法在四个基准数据集CASIA-FASD、REPLAY-ATTACK、MSU-MFSD和OULU-NPU上进行训练测试,分别达到了6.33%、12.05%、8.38%、10.59%的准确率,优于现有算法,表明该方法能够显著提升人脸活体检测模型在跨数据集测试中的泛化性能。

基于子带谐波一致性的语音转换反取证框架研究

语音转换任务指的是在保持语言内容不变的情况下,将一个说话者的声音身份转换为另一个说话者.然而现有工作很少考虑针对音频取证机器分类模型进行抗检测研究,转换音频极容易被取证模型所识别.本文提出了一种具有3个子带频谱鉴别器设计的语音转换反取证框架HADV-GAN,其合成音频在具有高保真度的前提下,对语音欺骗取证模型具有反取证能力.此外,HADV-GAN无需训练额外的声码器,可以直接以原始音频波形作为输入,并以声学特征重建语音,因此可以避免使用声码器所导致的特征不匹配问题.实验结果表明,本文所提出的方法在3种主流的语音欺骗取证模型LFCC-GMM、MCG-Res2Net以及AASIST上,对比基线模型NVC-Net,在合成音频质量相当的条件下,拥有更好的反取证能力.