scholarly journals Deep Discriminative Restricted Boltzmann Machine (DDRBM) for Robust Face Spoofing Detection

2018 ◽  
Vol 1 (3) ◽  
Author(s):  
Gustavo Botelho de Souza ◽  
Joao Paulo Papa ◽  
Aparecido Nilceu Marana
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Attack Detection ◽  
Restricted Boltzmann Machine ◽  
Digital Cameras ◽  
Boltzmann Machine ◽  
Robust Solution ◽  
Spoofing Detection ◽  
Recognition Systems ◽  
Face Spoofing

Biometrics emerged as a robust solution for security systems. Despite, nowadays criminals are developing techniques to accurately simulate biometric traits of valid users, process known as spoofing attack, in order to circumvent the biometric applications. Face is among the main biometric characteristics, being extremely convenient for users given its non-intrusive capture by means of digital cameras. However, face recognition systems are the ones that most suffer with spoofing attacks since such cameras, in general, can be easily fooled with common printed photographs. In this sense, countermeasure techniques should be developed and integrated to the traditional face recognition systems in order to prevent such frauds. Among the main neural networks for face spoofing detection is the discriminative Restricted Boltzmann Machine (RBM) which, besides of efficiency, achieves great results in attack detection by learning the distributions of real and fake facial images. However, it is known that deeper neural networks present better accuracy results in many tasks. In this context, we propose a novel model called Deep Discriminative Restricted Boltzmann Machine (DDRBM) applied to face spoofing detection. Results on the NUAA dataset show a significative improvement in performance when compared to the accuracy rates of a traditional discriminative RBM on attack detection.

Multispectral face spoofing detection using VIS–NIR imaging correlation

2018 ◽  
Vol 16 (02) ◽  
pp. 1840003 ◽  
Author(s):  
Xudong Sun ◽  
Lei Huang ◽  
Changping Liu
Keyword(s):  
Face Recognition ◽  
Near Infrared ◽  
Experimental Results ◽  
Security Systems ◽  
Local Descriptors ◽  
Confidence Factor ◽  
Nir Imaging ◽  
Spoofing Detection ◽  
Factor Map ◽  
Face Spoofing

With the wide applications of face recognition techniques, spoofing detection is playing an important role in the security systems and has drawn much attention. This research presents a multispectral face anti-spoofing method working with both visible (VIS) and near-infrared (NIR) spectra imaging, which exploits VIS–NIR image consistency for spoofing detection. First, we use part-based methods to extract illumination robust local descriptors, and then the consistency is calculated to perform spoofing detection. In order to further exploit multispectral correlation in local patches and to be free from manually chosen regions, we learn a confidence factor map for all the patches, which is used in final classifier. Experimental results of self-collected datasets, public Msspoof and PolyU-HSFD datasets show that the proposed approach gains promising results for both intra-dataset and cross-dataset testing scenarios, and that our method can deal with different illumination and both photo and screen spoofing.

scholarly journals MagNet: Detecting Digital Presentation Attacks on Face Recognition

2021 ◽  
Vol 4 ◽  
Author(s):  
Akshay Agarwal ◽  
Richa Singh ◽  
Mayank Vatsa ◽  
Afzel Noore
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Error Rates ◽  
Attack Detection ◽  
Limited Attention ◽  
Feature Descriptor ◽  
Security Risk ◽  
Learning Networks ◽  
Social Media Platforms ◽  
Recognition Systems

Presentation attacks on face recognition systems are classified into two categories: physical and digital. While much research has focused on physical attacks such as photo, replay, and mask attacks, digital attacks such as morphing have received limited attention. With the advancements in deep learning and computer vision algorithms, several easy-to-use applications are available where with few taps/clicks, an image can be easily and seamlessly altered. Moreover, generation of synthetic images or modifying images/videos (e.g. creating deepfakes) is relatively easy and highly effective due to the tremendous improvement in generative machine learning models. Many of these techniques can be used to attack the face recognition systems. To address this potential security risk, in this research, we present a novel algorithm for digital presentation attack detection, termed as MagNet, using a “Weighted Local Magnitude Pattern” (WLMP) feature descriptor. We also present a database, termed as IDAgender, which consists of three different subsets of swapping/morphing and neural face transformation. In contrast to existing research, which utilizes sophisticated machine learning networks for attack generation, the databases in this research are prepared using social media platforms that are readily available to everyone with and without any malicious intent. Experiments on the proposed database, FaceForensic database, GAN generated images, and real-world images/videos show the stimulating performance of the proposed algorithm. Through the extensive experiments, it is observed that the proposed algorithm not only yields lower error rates, but also provides computational efficiency.

scholarly journals Face Spoofing Detection using Mixed Feature with Deep Convolutional Neural Networks

2020 ◽  
Vol 8 (5) ◽  
pp. 3309-3314
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Attack Detection ◽  
Detection Methods ◽  
Deep Convolutional Neural Networks ◽  
Replay Attack ◽  
Multiple Features ◽  
Spoofing Detection ◽  
Occurrence Matrix ◽  
Face Spoofing

Nowadays, face biometric-based access control systems are becoming ubiquitous in daily life while they are still vulnerable to spoofing attacks. Developing robust and reliable methods to prevent such frauds is unavoidable. As deep learning techniques have achieved satisfactory performances in computer vision, they have also been applied to face spoofing detection. However, the numerous parameters in these deep learning-based detection methods cannot be updated to optimum due to limited data. In this paper,a highly accurate face spoof detection system using multiple features and deep learning is proposed. The input video is broken into frames using content-based frame extraction. From each frame, the face of the person is cropped.From the cropped images multiple features like Histogram of Gradients (HoG), Local Binary Pattern (LBP), Center Symmetric LBP (CSLBP), and Gray level co-occurrence Matrix (GLCM) are extracted to train the Convolutional Neural Network(CNN). Training and testing are performed separately by using collected sample data.Experiments on the standard spoof database called Replay-Attack database the proposed system outperform other state-of-the-art techniques, presenting great results in terms of attack detection.

scholarly journals Face Recognition Using Livenessnet

2021 ◽  
pp. 143-148
Author(s):  
Shajahan K ◽  
Rathish Rai D ◽  
Ravishankara
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Access Control ◽  
Smart Card ◽  
Deep Neural Networks ◽  
Digital Technologies ◽  
Recognition Method ◽  
Public Places ◽  
The Face ◽  
Face Spoofing

Every person's face is unique, although have the same structure such as noise, eyes, lips, etc. but it can vary strikingly. It’s within this variance which lies in the distinguishing characteristics that can be used to identify one person from another. Face recognition is a popular concept which is commonly used in surveillance cameras at public places for security purposes. With the advancement of digital technologies, the demand for security to provide access control is increasing. It uses various methods of authentication to keep all details secure, such as a system focused on encrypted user name & password, smart card, biometrics, etc. The “Face Recognition using DNN with LivenessNet” presents a face recognition method based on deep neural networks for liveness. Any algorithm is considered to be efficient only if it is robust and accurate. It provides accurate results with face spoofing quickly and efficiently. The main advantage of using this technique is identifying the uniqueness in the datasets by capturing the real-time face data through different modes & jitter. It provides accurate face recognition model which can be used for safety and security purpose.

scholarly journals A Review on Face Anti-Spoofing

2021 ◽  
Vol 5 (1) ◽  
pp. 29
Author(s):  
Rizky Naufal Perdana ◽  
Igi Ardiyanto ◽  
Hanung Adi Nugroho
Keyword(s):  
Face Recognition ◽  
Access Control ◽  
Real World ◽  
Biometric System ◽  
Security Issue ◽  
Security Technology ◽  
The Face ◽  
Paper Review ◽  
Recognition Systems ◽  
Face Spoofing

The biometric system is a security technology that uses information based on a living person's characteristics to verify or recognize the identity, such as facial recognition. Face recognition has numerous applications in the real world, such as access control and surveillance. But face recognition has a security issue of spoofing. A face anti-spoofing, a task to prevent fake authorization by breaching the face recognition systems using a photo, video, mask, or a different substitute for an authorized person's face, is used to overcome this challenge. There is also increasing research of new datasets by providing new types of attack or diversity to reach a better generalization. This paper review of the recent development includes a general understanding of face spoofing, anti-spoofing methods, and the latest development to solve the problem against various spoof types.

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