A Color-Texture-Based Deep Neural Network Technique to Detect Face Spoofing Attacks (original) (raw)

Deep Transfer Learning for Face Spoofing Detection

In recent years, the biometrics systems gained more popularity in the identification of the individual. In the biometric systems, the face is a widely used biometric trait in the authentication and surveillance systems. As the usage of face biometric traits getting increased in face recognition, the system becomes more vulnerable to spoofing attacks. In computer vision, the new techniques, algorithms, and advanced approaches are used for better accuracy and performance to develop the models that can be used in the anti-spoofing to tackle any kind of spoofing attack. In this paper, we have has implemented the module to detect face anti-spoofing attacks that may possible through printed or mobile photos by the imposter. To implement this module the deep neural network and transfer learning approach is used. The pre-trained models are used to train the anti-spoofing module for detecting spoofing attacks. The different models are trained and tested on the NUAA dataset and the model with the highest accuracy is used in the anti-spoofing module. In this research paper, we have used the VGG16 model to detect spoofing attacks. The highest validation accuracy was achieved for VGG 16 model 100% on the NUAA dataset.

Transfer Learning Using Convolutional Neural Networks for Face Anti-spoofing

Lecture Notes in Computer Science, 2017

Face recognition systems are gaining momentum with current developments in computer vision. At the same time, tactics to mislead these systems are getting more complex, and countermeasure approaches are necessary. Following the current progress with convolutional neural networks (CNN) in classification tasks, we present an approach based on transfer learning using a pre-trained CNN model using only static features to recognize photo, video or mask attacks. We tested our approach on the REPLAY-ATTACK and 3DMAD public databases. On the REPLAY-ATTACK database our accuracy was 99.04% and the half total error rate (HTER) of 1.20%. For the 3DMAD, our accuracy was of 100.00% and HTER 0.00%. Our results are comparable to the state-of-the-art.

The Classification Method for the Identification of Face Spoof in Convolutional Neural Networks

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021

Automatic facial recognition is currently extensively utilised in a variety of applications, ranging from identity deduplication to mobile payment verification. Face recognition has grown in popularity, raising worries about face spoof attacks (also known as biometric sensor presentation assaults), in which a picture or video of an authorised person's face may be used to obtain access to facilities or services without the person's knowledge. Even though a lot of face spoof detection methods have been suggested, their capacity to generalise has not been well investigated. On the basis of Image Distortion Analysis(IDA), we present an efficient and somewhat robust face spoof detection method . A new paradigm for each stage of a face recognition system is introduced in this article. In the phase of face identification, we present a hybrid model that combines AdaBoost and Convolutional Neural Network (ABCNN) to effectively handle the procedure. A multilayer perceptron and an active shape model will be used in conjunction with an ABANN to align the labelled faces identified in the previous phase. A mixture of Dense and Convolutional neural network layers was used to achieve binary classification of false recognition. The accuracy of categorial cross entropy prediction in Adam was found to be 91 percent, while the accuracy of SGD (stochastic gradient descent) was found to be 88 percent. In binary cross entropy, 90 percent accuracy was seen in Adam and 86 percent accuracy was observed in SGD, while in mean square, 86 percent accuracy was observed in Adam and 80 percent accuracy was observed in SGD.

Score-Level-based Face Anti-Spoofing System Using Handcrafted and Deep Learned Characteristics

International Journal of Image, Graphics and Signal Processing, 2019

Recognition performance of biometric systems is affected through spoofing attacks made by fake identities. The focus of this paper is on presenting a new scheme based on score level and decision level fusion to monitor individuals in term of real and fake. The proposed fake detection scheme involve consideration of both handcrafted and deep learned techniques on face images to differentiate real and fake individuals. In this approach, convolutional neural network (CNN) and overlapped histograms of local binary patterns (OVLBP) methods is used to extract facial features of images. The produced matching scores provided by CNN and OVLBP then combined to form a fused score vector. Finally, the last decision on real and attack images is done by combining decisions of hybrid scheme using majority vote of CNN, OVLBP and their fused vector. Experimental results on public spoof databases such as Print-Attack and Replay-Attack face databases demonstrate the strength of the proposed anti-spoofing method for fake detection.

Face Spoof Attack Detection using Deep Background Subtraction

Currently, face recognition technologies are the most widely used methods for verifying 1an individual’s identity. Nevertheless, it has increased in popularity, raising concerns about face spoofing attacks, in which a photo or video of an authorized person’s face is used to get access to services. Based on a combination of Background Subtraction (BS) and Convolutional Neural Networks (CNN), as well as an ensemble of classifiers, we propose an efficient and more robust face spoof detection algorithm. This algorithm includes a Fully Connected (FC) classifier with a Majority Vote (MV) algorithm, which uses different face spoof attacks (e.g., printed photo and replayed video). By including a majority vote to determine whether the input video is genuine or not, the proposed method significantly enhances the performance of the Face Anti-Spoofing (FAS) system. For evaluation, we considered the MSU MFSD, REPLAY-ATTACK, and CASIA-FASD databases. The obtained results by our proposed approach ...

Unraveling robustness of deep face anti-spoofing models against pixel attacks

Multimedia Tools and Applications, 2020

In the last few decades, deep-learning-based face verification and recognition systems have had enormous success in solving complex security problems. However, it has been recently shown that such efficient frameworks are vulnerable to face-spoofing attacks, which has led researchers to build proficient anti-facial-spoofing (or liveness detection) models as an additional security layer. In response, increasingly challenging and tricky attacks have been launched to fool these anti-spoofing mechanisms. In this context, this paper presents the results of an analytical study on transfer-learning-based convolutional neural networks (CNNs) for face liveness detection and differential evolution-based adversarial attacks to evaluate the efficiency of face anti-spoofing classifiers against adversarial attacks. Specifically, experiments were conducted under different use-case scenarios on four face anti-spoofing databases to highlight practical criteria that can be used in the development of countermeasures to address face-spoofing issues.

Face Spoofing Detection using Enhanced Local Binary Pattern

International Journal of Engineering and Advanced Technology, 2019

Among various biometric systems, over the past few years identifying the face patterns has become the centre of attraction, owing to this, a substantial improvement has been made in this area. However, the security of such systems may be a crucial issue since it is proved in many studies that face identification systems are susceptible to various attacks, out of which spoofing attacks are one of them. Spoofing is defined as the capability of making fool of a system that is biometric for finding out the unauthorised customers as an actual one by the various ways of representing version of synthetic forged of the original biometric trait to the sensing objects. In order to guard face spoofing, several anti-spoofing methods are developed to do liveliness detection. Various techniquesfordetection of spoofing make the use of LBP i.e. local binary patterns that make the difference to symbolise handcrafted texture features from images, whereas, recent researches have shown that deep featur...

A Case Study on Face Spoof Detection

IRJET, 2022

User authentication is a vital step in protecting information, and facial bio metrics might assist in this regard. Face bio metrics seems to be more natural, simple to use, and less intrusive to humans. Unfortunately, emerging research has revealed that face bio metrics are extremely sensitive to spoofing assaults. A spoofing attack occurs when a person tries to masquerade as someone else by falsifying data and thereby gaining illegitimate access. Inspired by image quality assessment, characterization of printing artifacts, and differences in light reflection, we propose to approach the problem of spoofing detection from texture analysis point of view. This report discusses many types of assaults against visual spectrum facial recognition systems. We propose comprehensive data sets for assessing the susceptibility of recognition systems and the effectiveness of countermeasures. Finally, we give a brief overview of anti-spoofing strategies for visual spectrum face identification, as well as a viewpoint on difficulties that remain unresolved.

IoT Cloud-Based Framework for Face Spoofing Detection with Deep Multicolor Feature Learning Model

Journal of Sensors, 2021

A face-based authentication system has become an important topic in various fields of IoT applications such as identity validation for social care, crime detection, ATM access, computer security, etc. However, these authentication systems are vulnerable to different attacks. Presentation attacks have become a clear threat for facial biometric-based authentication and security applications. To address this issue, we proposed a deep learning approach for face spoofing detection systems in IoT cloud-based environment. The deep learning approach extracted features from multicolor space to obtain more information from the input face image regarding luminance and chrominance data. These features are combined and selected by the Minimum Redundancy Maximum Relevance (mRMR) algorithm to provide an efficient and discriminate feature set. Finally, the extracted deep color-based features of the face image are used for face spoofing detection in a cloud environment. The proposed method achieves ...

Face recognition under spoofing attacks: countermeasures and research directions

IET Biometrics, 2018

Among tangible threats facing current biometric systems are spoofing attacks. A spoofing attack occurs when a person tries to masquerade as someone else by falsifying data and thereby attempting to gain illegitimate access and advantages. Recently, an increasing attention has been given to this research problem, as can be attested by the growing number of articles and the various competitions that appear in major biometric forums. This study presents a comprehensive overview of the recent advances in face anti-spoofing state-of-the-art, discussing existing methodologies, available benchmarking databases, reported results and, more importantly, the open issues and future research directions. As a case study for illustration, a face anti-spoofing method is described, which employs a colour local binary pattern descriptor to jointly analyse colour and texture available from the luminance and chrominance channels. Two publicly available databases are used for the analysis, and the importance of inter-database evaluation to attest the generalisation capabilities of an anti-spoofing method is discussed.