Face Recognition on Consumer Devices: Reflections on Replay Attacks

Smith, Dianne; Wiliem, Arnold; Lovell, Brian C.

doi:10.1109/tifs.2015.2398819

Cited by 100 publications

(41 citation statements)

References 27 publications

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“…The authors of [36] propose to illuminate the face during image capture. The reflected color from the face served as a means to watermark the image.…”

Section: Related Workmentioning

confidence: 99%

Face Spoofing Detection Based on Multiple Descriptor Fusion Using Multiscale Dynamic Binarized Statistical Image Features

Arashloo

Kittler

Christmas

2015

IEEE Trans.Inform.Forensic Secur.

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Abstract-Face recognition has been the focus of attention for the past couple of decades and, as a result, a significant progress has been made in this area. However, the problem of spoofing attacks can challenge face biometric systems in practical applications. In this work, an effective countermeasure against face spoofing attacks based on a kernel discriminant analysis approach is presented. Its success derives from three innovations. First it is shown that the recently proposed multiscale dynamic texture descriptor based on binarized statistical image features on three orthogonal planes (MBSIF-TOP) is effective in detecting spoofing attacks, showing promising performance compared to existing alternatives.Next, by combining MBSIF-TOP with a blur-tolerant descriptor, namely the dynamic multiscale local phase quantization representation (MLPQ-TOP), the robustness of the spoofing attack detector can be further improved. The fusion of the information provided by MSIF-TOP and MLPQ-TOP is realized via a kernel fusion approach based on a fast kernel discriminant analysis (KDA) technique. It avoids the costly eigenanalysis computations by solving the KDA problem via spectral regression. The experimental evaluation of the proposed system on different databases demonstrates its advantages in detecting spoofing attacks in various imaging conditions, compared to existing methods.

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“…The authors of [36] propose to illuminate the face during image capture. The reflected color from the face served as a means to watermark the image.…”

Section: Related Workmentioning

confidence: 99%

Face Spoofing Detection Based on Multiple Descriptor Fusion Using Multiscale Dynamic Binarized Statistical Image Features

Arashloo

Kittler

Christmas

2015

IEEE Trans.Inform.Forensic Secur.

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“…Spoofing attacks are of concern with respect to face recognition security, but are taken into consideration by the research community [50], [51]. There are plenty of techniques such as liveness detection [52] and progressive authentication [53] which can be easily integrated in the face recognition module to counter-attack spoofing.…”

Section: Providing An Appropriate Mechanism For Anti-impersonationmentioning

confidence: 99%

Designing a Secure Exam Management System (SEMS) for M-Learning Environments

Kaiiali

Ozkaya

Altun

et al. 2016

IEEE Trans. Learning Technol.

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Abstract-M-learning has enhanced the e-learning by making the learning process learner-centered. However, enforcing exam security in open environments where each student has his/her own mobile/tablet device connected to a Wi-Fi network through which it is further connected to the Internet can be one of the most challenging tasks. In such environments, students can easily exchange information over the network during exam time. This paper aims to identify various vulnerabilities that may violate exam security in mlearning environments and to design the appropriate security services and countermeasures that can be put in place to ensure exam security. It also aims to integrate the resulting secure exam system with an existing, open-source, and widely accepted Learning Management System (LMS) and its service extension to the m-learning environment, namely "the Moodbile Project".

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“…These encouraging results may pave the way for further studies in theuse of video analysis for defeating biometric replay attacks on consumerdevices. [8] 9. Secure Online Exams Using Students Devices (2012)Authors: G. Frankl, P. Schartner, and G. Zebedin With the augmented use of Learning Management Systems (LMS) likeMoodle, the demand to perform exams online is higher than ever.…”

Section: "The Social Mobile Learning Experiences Ofstudents Using Mobmentioning

confidence: 99%

Simulated Educational Exam System Using Iot

2016

IJAERD

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Abstract-The Internet of Things(IOT) is the inter-networking of physical devices and it provides the ability to transfer a data over a network.In general examination system,there are more chances to get paper hacked i.e. security issues as well as unauthorized access to exam module.to overcome this problems,we are going to provide an efficient education system which helps to avoid the security issues. We erase also generating certificates.

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Face Recognition on Consumer Devices: Reflections on Replay Attacks

Cited by 100 publications

References 27 publications

Face Spoofing Detection Based on Multiple Descriptor Fusion Using Multiscale Dynamic Binarized Statistical Image Features

Face Spoofing Detection Based on Multiple Descriptor Fusion Using Multiscale Dynamic Binarized Statistical Image Features

Designing a Secure Exam Management System (SEMS) for M-Learning Environments

Simulated Educational Exam System Using Iot

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