Competition on counter measures to 2-D facial spoofing attacks

Chakka, Murali Mohan; Anjos, André; Marcel, Sébastien; Tronci, Roberto; Muntoni, Daniele; Fadda, Gianluca; Pili, Maurizio; Sirena, N.; Murgia, Gabriele; Ristori, Marco; Roli, Fabio; Yan, Junjie; Dong, Yi; Lei, Zhen; Zhang, Zhiwei; Li, Stan Z.; Schwartz, William Robson; Rocha, Anderson; Pedrini, Hélio; Lorenzo-Navarro, Javier; Castrillón-Santana, Modesto; Määttä, Jukka; Hadid, Abdenour; Pietikäinen, Matti

doi:10.1109/ijcb.2011.6117509

Cited by 122 publications

(152 citation statements)

References 27 publications

(23 reference statements)

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“…It was not until very recently that the problem of spoofing attacks against face biometric system gained attention of the research community. This can be attested by the gradually increasing number of publicly available databases [3][4][5][6] and the recently organized IJCB 2011 competition on countermeasures to 2-D facial spoofing attacks [7] which was the first competition conducted for studying best practices for non-intrusive spoofing detection.…”

Section: Introductionmentioning

confidence: 99%

Face liveness detection using dynamic texture

et al. 2014

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User authentication is an important step to protect information, and in this context, face biometrics is potentially advantageous. Face biometrics is natural, intuitive, easy to use, and less human-invasive. Unfortunately, recent work has revealed that face biometrics is vulnerable to spoofing attacks using cheap low-tech equipment. This paper introduces a novel and appealing approach to detect face spoofing using the spatiotemporal (dynamic texture) extensions of the highly popular local binary pattern operator. The key idea of the approach is to learn and detect the structure and the dynamics of the facial micro-textures that characterise real faces but not fake ones. We evaluated the approach with two publicly available databases (Replay-Attack Database and CASIA Face Anti-Spoofing Database). The results show that our approach performs better than state-of-the-art techniques following the provided evaluation protocols of each database.

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Section: Introductionmentioning

confidence: 99%

Face liveness detection using dynamic texture

et al. 2014

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“…A recently organized competition on a subset of the PHOTO-ATTACK database called PRINT-ATTACK [30] showed that texture-based methods could potentially beat our proposed method under those conditions. To estimate how well such methods can work against the PHOTO-ATTACK database, we evaluated the work of Chingovska and others [31], a variant of the competition winner based on Local Binary Patterns (LBP), and to which we have access to an open-source implementation 6 .…”

Section: ) Comparison To Texture Approachesmentioning

confidence: 99%

Motion‐based counter‐measures to photo attacks in face recognition

2014

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Identity spoofing is a contender for high-security face recognition applications. With the advent of social media and globalized search, our face images and videos are wide-spread on the internet and can be potentially used to attack biometric systems without previous user consent. Yet, research to counter these threats is just on its infancy -we lack public standard databases, protocols to measure spoofing vulnerability and baseline methods to detect these attacks. The contributions of this work to the area are three-fold: firstly we introduce a publicly available PHOTO-ATTACK database with associated protocols to measure the effectiveness of counter-measures. Based on the data available, we conduct a study on current state-of-the-art spoofing detection algorithms based on motion analysis, showing they fail under the light of these new dataset. By last, we propose a new technique of countermeasure solely based on foreground/background motion correlation using Optical Flow that outperforms all other algorithms achieving nearly perfect scoring with an equal-error rate of 1.52% on the available test data. The source code leading to the reported results is made available for the replicability of findings in this article.

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“…However, reflectance and texture analysis-based defense mechanisms have also been proposed against 3D mask attacks [17]- [20]. It is worth noting that many different approaches and design choices have been proposed at the competitions on the countermeasures to presentation attacks [21]- [23].…”

Section: A Presentation Attacks and Defensesmentioning

confidence: 99%

rtCaptcha: A Real-Time CAPTCHA Based Liveness Detection System

Uzun¹

2018

Proceedings 2018 Network and Distributed System Security Symposium

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Abstract-Facial/voice-based authentication is becoming increasingly popular (e.g., already adopted by MasterCard and AliPay), because it is easy to use. In particular, users can now authenticate themselves to online services by using their mobile phone to show themselves performing simple tasks like blinking or smiling in front of its built-in camera. Our study shows that many of the publicly available facial/voice recognition services (e.g. Microsoft Cognitive Services or Amazon Rekognition) are vulnerable to even the most primitive attacks. Furthermore, recent work on modeling a person's face/voice (e.g. Face2Face [1]) allows an adversary to create very authentic video/audio of any target victim to impersonate that target. All it takes to launch such attacks are a few pictures and voice samples of a victim, which can all be obtained by either abusing the camera and microphone of the victim's phone, or through the victim's social media account. In this work, we propose the Real Time Captcha (rtCaptcha) system, which stops/slows down such an attack by turning the adversary's task from creating authentic video/audio of the target victim performing known authentication tasks (e.g., smile, blink) to figuring out what is the authentication task, which is encoded as a Captcha. Specifically, when a user tries to authenticate using rtCaptcha, they will be presented a Captcha and will be asked to take a "selfie" video while announcing the answer to the Captcha. As such, the security guarantee of our system comes from the strength of Captcha, and not how well we can distinguish real faces/voices from synthesized ones. To demonstrate the usability and security of rtCaptcha, we conducted a user study to measure human response times to the most popular Captcha schemes. Our experiments show that, thanks to the humans' speed of solving Captchas, adversaries will have to solve Captchas in less than 2 seconds in order to appear live/human and defeat rtCaptcha, which is not possible for the best settings on the attack side.

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Competition on counter measures to 2-D facial spoofing attacks

Cited by 122 publications

References 27 publications

Face liveness detection using dynamic texture

Face liveness detection using dynamic texture

Motion‐based counter‐measures to photo attacks in face recognition

rtCaptcha: A Real-Time CAPTCHA Based Liveness Detection System

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