Anti-spoofing enabled face recognition based on aggregated local weighted gradient orientation

Spoofing attack is a catastrophic threat for biometric authentication systems. Inspired by the concept of depth map estimation, a novel anti-spoofing technique based on aggregated local weighted gradient orientation (ALWGO) is proposed. We first estimate the depth of the specimen face image. In the next step, highly discriminant ALWGO features are extracted from the depth map. Finally, a sparse representation classifier is trained to distinguish between the genuine and fake faces. This paper particularly addresses the potential of texture gradient features and their variations, on three types of attacks, viz. printed high-definition photographs, warped photographs and videos displayed on mobile phones. The usage of ALWGO features has been extended for further face recognition. Our proposed approach is robust and nonintrusive as compared to many existing methods. Extensive experimental analysis on publicly available databases clearly demonstrates the superiority of our approach for both face spoofing detection and recognition systems.     

Deep face liveness detection based on nonlinear diffusion using convolution neural network

A face-spoofing attack occurs when an imposter manipulates a face recognition and verification system to gain access as a legitimate user by presenting a 2D printed image or recorded video to the face sensor. This paper presents an efficient and non-intrusive method to counter face-spoofing attacks that uses a single image to detect spoofing attacks. We apply a nonlinear diffusion based on an additive operator splitting scheme. Additionally, we propose a specialized deep convolution neural network that can extract the discriminative and high-level features of the input diffused image to differentiate between a fake face and a real face. Our proposed method is both efficient and convenient compared with the previously implemented state-of-the-art methods described in the literature review. We achieved the highest reported accuracy of 99% on the widely used NUAA dataset. In addition, we tested our method on the Replay Attack dataset which consists of 1200 short videos of both real access and spoofing attacks. An extensive experimental analysis was conducted that demonstrated better results when compared to previous static algorithms results. However, this result can be improved by applying a sparse autoencoder learning algorithm to obtain a more distinguishable diffused image.     

基于图像分割网络的深度假脸视频篡改检测

随着深度学习技术的快速发展,利用深度神经网络模型伪造出的深度假脸(deepfake)视频越来越逼真,假脸视频造成的威胁也越来越大。文献中已出现一些基于卷积神经网络的换脸视频检测算法,他们在库内获得较好的检测效果,但跨库检测性能急剧下降,存在泛化能力不足的问题。该文从假脸篡改的机制出发,将视频换脸视为特殊的拼接篡改问题,利用流行的神经分割网络首先预测篡改区域,得到预测掩膜概率图,去噪并二值化,然后根据换脸主要发生在人脸区域的前提,提出一种计算人脸交并比的新方法,并进一步根据换脸处理的先验知识改进人脸交并比的计算,将其作为篡改检测的分类准则。所提出方法分别在3个不同的基础分割网络上实现,并在TIMIT, FaceForensics++, FFW数据库上进行了实验,与文献中流行的同类方法相比,在保持库内检测的高准确率同时,跨库检测的平均错误率显著下降。在近期发布的合成质量较高的DFD数据库上也获得了很好的检测性能,充分证明了所提出方法的有效性和通用性。     

Integration of image quality and motion cues for face anti-spoofing: A neural network approach

Many trait-specific countermeasures to face spoofing attacks have been developed for security of face authentication. However, there is no superior face anti-spoofing technique to deal with every kind of spoofing attack in varying scenarios. In order to improve the generalization ability of face anti-spoofing approaches, an extendable multi-cues integration framework for face anti-spoofing using a hierarchical neural network is proposed, which can fuse image quality cues and motion cues for liveness detection. Shearlet is utilized to develop an image quality-based liveness feature. Dense optical flow is utilized to extract motion-based liveness features. A bottleneck feature fusion strategy can integrate different liveness features effectively. The proposed approach was evaluated on three public face anti-spoofing databases. A half total error rate (HTER) of 0% and an equal error rate (EER) of 0% were achieved on both REPLAY-ATTACK database and 3D-MAD database. An EER of 5.83% was achieved on CASIA-FASD database.     

身份认证中灰度共生矩阵和小波分析的活体人脸检测算法

随着身份认证技术的广泛应用,各种假冒合法用户欺骗身份认证系统的手段不断出现。针对这一问题,本文提出了一种基于灰度共生矩阵和小波分析的活体照片人脸检测方法,该方法分析了活体人脸和照片人脸成像后在纹理上的差异性,在人脸灰度共生矩阵的基础上提取能量、熵、惯性矩和相关性四个纹理特征量;同时利用小波变换对人脸图像进行二级分解,提取高频子带系数作为特征向量训练SVM分类识别,算法在公开的数据库NUAA上进行了验证,实验结果表明该方法降低了计算复杂度,提高了检测准确率。      

Wavelet based fingerprint liveness detection

Proposed is a simple and effective approach for fingerprint liveness detection based on the wavelet analysis of the finger tip surface texture. Experimental results show that the method can successfully differentiate live finger tips from fake finger tips made of the most commonly used materials in fingerprint spoofing.     

语音伪造及检测技术研究综述

语音承载着人类语言和说话人身份信息,通过语音伪造技术可以精确模仿目标说话人的声音以达到欺骗人或机器听觉的目的。目前,深度伪造（Deepfake）正在对全球的政治经济及社会稳定带来极大的威胁,其中语音伪造是Deepfake实现舆论操控的核心技术之一。近年来语音伪造技术在拟人度、自然度方面有了显著进步,使得语音伪造检测技术面临着更大的挑战。本文对当前主流的语音伪造和伪造语音检测技术研究现状进行综述,主要包括:1）对主流语音伪造技术,包括语音合成、语音转换和语音对抗样本的基本概念、技术发展历程和研究进展进行综述;2）对伪造语音检测技术的基本概念、性能评价指标、主要技术实现原理和性能效果进行综述;3）对伪造语音检测相关的主流竞赛、常用数据集和可用代码工具资源进行介绍;最后对语音伪造和检测技术现存的挑战性问题和未来的研究方向进行讨论。      

人脸欺诈检测最新进展及典型方法

以人脸为生物特征的身份认证由于其简单、快捷和无接触，深受用户喜爱。然而，不法分子针对人脸的易于伪造和认证场景的多样性，恶意攻击人脸识别系统，给国家安全和人民生活带来极大危害，有必要深入研究人脸的反欺诈技术。该文收集了经典和代表性论文68篇，其中包括近2年来国际主流期刊和会议的相关论文50余篇，从7大类来梳理近期人脸欺诈检测的技术脉络，把握领域的最新动向。为了使读者洞悉各类的技术精髓，对典型方法进行了重点介绍，并对各类进行了技术小结。在方法介绍结束之后，对2019年以来新推出的6个人脸欺诈检测数据库进行了简要介绍，便于读者对本领域的最新算法开发和验证平台有所了解。结语部分对人脸欺诈检测7类方法之间的关联、在理论和应用上的总体现状、存在的主要问题以及发展的方向作了简单的总结和展望。      

Improved Two‐Phase Framework for Facial Emotion Recognition

Automatic emotion recognition based on facial cues, such as facial action units (AUs), has received huge attention in the last decade due to its wide variety of applications. Current computer‐based automated two‐phase facial emotion recognition procedures first detect AUs from input images and then infer target emotions from the detected AUs. However, more robust AU detection and AU‐to‐emotion mapping methods are required to deal with the error accumulation problem inherent in the multiphase scheme. Motivated by our key observation that a single AU detector does not perform equally well for all AUs, we propose a novel two‐phase facial emotion recognition framework, where the presence of AUs is detected by group decisions of multiple AU detectors and a target emotion is inferred from the combined AU detection decisions. Our emotion recognition framework consists of three major components — multiple AU detection, AU detection fusion, and AU‐to‐emotion mapping. The experimental results on two real‐world face databases demonstrate an improved performance over the previous two‐phase method using a single AU detector in terms of both AU detection accuracy and correct emotion recognition rate.     

High‐throughput and low‐area implementation of orthogonal matching pursuit algorithm for compressive sensing reconstruction

Massive computation of the reconstruction algorithm for compressive sensing (CS) has been a major concern for its real‐time application. In this paper, we propose a novel high‐speed architecture for the orthogonal matching pursuit (OMP) algorithm, which is the most frequently used to reconstruct compressively sensed signals. The proposed design offers a very high throughput and includes an innovative pipeline architecture and scheduling algorithm. Least‐squares problem solving, which requires a huge amount of computations in the OMP, is implemented by using systolic arrays with four new processing elements. In addition, a distributed‐arithmetic‐based circuit for matrix multiplication is proposed to counterbalance the area overhead caused by the multi‐stage pipelining. The results of logic synthesis show that the proposed design reconstructs signals nearly 19 times faster while occupying an only 1.06 times larger area than the existing designs for N = 256, M = 64, and m = 16, where N is the number of the original samples, M is the length of the measurement vector, and m is the sparsity level of the signal.     

Array‐based reader anti‐collision scheme for highly efficient RFID network applications

Radio frequency identification (RFID) is a technology for identifying objects by radio signal. The reader collision problem is caused by the interrogation zones overlap of multiple readers, and leading to incorrect and unreliable data. This study proposes an array‐based reader anti‐collision scheme (ARCS) for solving the reader‐to‐tag interference problem. This mechanism uses array and grouping techniques to schedule the active readers to reduce the read cycle (RC) time. The ‘RC’ and ‘collision ratio (CR)’ measures serve as performance indexes in the proposed scheme. From ‘RC’ comparison, the ARCS mechanism has the maximum improvement of 55 per cent as compared with the existing mechanisms. Furthermore, take ‘CR’ index, ARCS has the maximum improvement of 42 per cent as compared to the others. Copyright © 2008 John Wiley & Sons, Ltd.     

基于部分标记数据进行人脸图像特征提取

针对无监督特征提取的识别率低与监督特征提取需要大量标记的问题,提出一种基于部分标记数据的半监督判别分析(SSDPA)特征提取法。本文方法能实现图像数据降维,避免线性判别分析(LDA)存在的小样本问题,达到提高识别率的目的。算法对图像进行离散余弦变换(DCT)变换;根据DCT图像的频率分布,利用部分标记数据计算SSDP;优先搜索SSDP高的DCT图像信息。将本文方法与其它方法进行组合,在不同人脸数据库上进行了实验。实验证明了本文方法的有效性,用较低的代价获得了优于传统方法的识别率。     

基于深度学习的人脸识别方法研究

作为非接触式生物识别方法之一,人脸识别在诸多情况下被广泛使用。然而,传统的人脸识别方法由于识别准确度低以及在多个场合的应用受到限制,已不能满足目前的需求。文中提出了采用深度学习的方法来实现脸部标志检测和无限制人脸识别。为解决人脸标志检测问题,使用一种深层卷积神经网络的逐层训练方法,以帮助卷积神经网络进行收敛,并提出了一种避免过拟合的样本变换方法;为了解决人脸识别问题,文中提出了一种SIAMESE卷积神经网络,其在不同部位和尺度上进行训练。实验测试显示,ORL和人脸识别算法的精度分别达到了91%和81%。     

FuzMet: a fuzzy‐logic based alert prioritization engine for intrusion detection systems

Intrusion detection systems (IDSs) are designed to monitor a networked environment and generate alerts whenever abnormal activities are detected. The number of these alerts can be very large, making their evaluation by security analysts a difficult task. Management is complicated by the need to configure the different components of alert evaluation systems. In addition, IDS alert management techniques, such as clustering and correlation, suffer from involving unrelated alerts in their processes and consequently provide results that are inaccurate and difficult to manage. Thus the tuning of an IDS alert management system in order to provide optimal results remains a major challenge, which is further complicated by the large spectrum of potential attacks the system can be subject to. This paper considers the specification and configuration issues of FuzMet, a novel IDS alert management system which employs several metrics and a fuzzy‐logic based approach for scoring and prioritizing alerts. In addition, it features an alert rescoring technique that leads to a further reduction in the number of alerts. Comparative results between SNORT scores and FuzMet alert prioritization onto a real attack dataset are presented, along with a simulation‐based investigation of the optimal configuration of FuzMet. The results prove the enhanced intrusion detection accuracy brought by our system. Copyright © 2011 John Wiley & Sons, Ltd.     

基于IMM-KF算法改进的欺骗式干扰检测算法

针对基于接收机基线长度的欺骗干扰检测方法在短基线和低定位精度下检测性能差的问题,提出了一种交互多模型卡尔曼滤波（Interactive Multi-model Kalman Filtering,IMM-KF）算法改进的欺骗式干扰检测算法。该方法通过IMM-KF算法对两个接收机天线的位置信息进行最优估计,提高基线解算精度,从而提升基于基线长度的欺骗干扰检测方法的检测性能。首先,详细分析了基于基线长度的欺骗干扰检测方法的数学原理;其次,建立欺骗检测算法模型,并对其性能进行仿真分析;然后,根据算法应用场景引入IMM-KF算法优化基线长度估计量;最后,针对不同的基线长度和定位精度进行仿真实验,对算法进行性能评估。仿真结果表明,该算法可以在接收机伪距测量精度为0.1 m、基线长度为0.5 m的情况下达到86%的检测成功率,而传统算法在此情况下检测成功率仅为10%。     

Robust Multi‐person Tracking for Real‐Time Intelligent Video Surveillance

We propose a novel multiple‐object tracking algorithm for real‐time intelligent video surveillance. We adopt particle filtering as our tracking framework. Background modeling and subtraction are used to generate a region of interest. A two‐step pedestrian detection is employed to reduce the computation time of the algorithm, and an iterative particle repropagation method is proposed to enhance its tracking accuracy. A matching score for greedy data association is proposed to assign the detection results of the two‐step pedestrian detector to trackers. Various experimental results demonstrate that the proposed algorithm tracks multiple objects accurately and precisely in real time.     

Real‐Time License Plate Detection in High‐Resolution Videos Using Fastest Available Cascade Classifier and Core Patterns

We present a novel method for real‐time automatic license plate detection in high‐resolution videos. Although there have been extensive studies of license plate detection since the 1970s, the suggested approaches resulting from such studies have difficulties in processing high‐resolution imagery in real‐time. Herein, we propose a novel cascade structure, the fastest classifier available, by rejecting false positives most efficiently. Furthermore, we train the classifier using the core patterns of various types of license plates, improving both the computation load and the accuracy of license plate detection. To show its superiority, our approach is compared with other state‐of‐the‐art approaches. In addition, we collected 20,000 images including license plates from real traffic scenes for comprehensive experiments. The results show that our proposed approach significantly reduces the computational load in comparison to the other state‐of‐the‐art approaches, with comparable performance accuracy.     

一种基于特征点对齐的假脸检测框架

近年来,Deepfake等假脸技术的产生颠覆了人们对人脸信息真实性和安全性的认知,引发广泛的社会担忧,检测假脸成为了学术界、产业界共同关注的热点问题。通过一种基于特征点对齐的假脸检测框架,可以有效对Deepfake技术产生的假脸进行判别。该框架制定了一套包括人脸检测、定点、对齐、特征提取、假脸识别等步骤的假脸检测流程,并通过引入特征点对齐保障假脸检测效果。在Deepfake检测挑战赛(DFDC)数据集上的试验表明,该框架适配4种当前主流骨干网络算法都能获得较好的检测结果;在FaceForensics++数据集上的试验表明,该框架适配ResNet50针对几种不同方式生成的假脸图像都可以取得良好效果。