Avoiding replay-attacks in a face recognition system using head-pose estimation

Frischholz, Robert; Werner, A.

doi:10.1109/amfg.2003.1240849

Cited by 57 publications

(33 citation statements)

References 2 publications

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“…Frischholz et al [8] investigate a challenge-response approach to enhance the security of the face recognition system. The users are required to look in certain directions, which were chosen by the system randomly.…”

Section: Related Workmentioning

confidence: 99%

Liveness Detection Using Gaze Collinearity

Ali

Deravi

Hoque

2012

2012 Third International Conference on Emerging Security Technologies

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This paper presents a liveness detection method based on tracking the gaze of the user of a face recognition system using a single camera. The user is required to follow a visual animation of a moving object on a display screen while his/her gaze is measured. The visual stimulus is designed to direct the gaze of the user to sets of collinear points on the screen. Features based on the measured collinearity of the observed gaze are then used to discriminate between live attempts at responding to this challenge and those conducted by "impostors" holding photographs and attempting to follow the stimulus. An initial set of experiments is reported that indicates the effectiveness of the proposed method in detecting this class of spoofing attacks

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Section: Related Workmentioning

confidence: 99%

Liveness Detection Using Gaze Collinearity

Ali

Deravi

Hoque

2012

2012 Third International Conference on Emerging Security Technologies

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“…Frischholz et al [12] investigated a challenge/response approach to enhance the security of the face recognition system. The users were required to look in certain directions, which were chosen by the system randomly.…”

Section: Introductionmentioning

confidence: 99%

Directional Sensitivity of Gaze-Collinearity Features in Liveness Detection

Ali

Deravi

Hoque

2013

2013 Fourth International Conference on Emerging Security Technologies

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-To increase the trust in using face recognition systems, these need to be capable of differentiating between face images captured from a real person and those captured from photos or similar artifacts presented at the sensor. Methods have been published for face liveness detection by measuring the gaze of a user while the user tracks an object on the screen, which appears at pre-defined, places randomly. In this paper we explore the sensitivity of such a system to different stimulus alignments. The aim is to establish whether there is such sensitivity and if so to explore how this may be exploited for improving the design of the stimulus. The results suggest that collecting feature points along the horizontal direction is more effective than the vertical direction for liveness detection.

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“…Requiring the users to engage in cooperative actions such as directed head movements [68], random head movements [52], eye blinking [164], or eye movement [107] takes time to complete and reduces the usability of the system as users are not motivated to engage in security processes before performing transactions [86,244].…”

Section: Digital Replay Attacksmentioning

confidence: 99%

“…Suggested countermeasures to digital replay attacks are to operate in a controlled environment [21], deploy specialised intelligent sensor hardware with specialised processor chips that can perform complex encryption or challenge-response operations [21,111,124,179,208], or involve the user in cooperative actions [68,107,164], but these techniques have not been extensively tested. All of these techniques are expensive or influence the usability of the system, limiting the widespread adoption of biometrics.…”

Section: Digital Replay Attacksmentioning

confidence: 99%

“…A number of techniques to detect "liveness" in face verification systems have been proposed, including distorting images [220], observing eye movement and eye blinks [102,107,164], head pose estimation [68], face movement detection based upon optical flows [14,117] and 3D geometry [52], analysis of high frequency Fourier spectra [127], image quality [73], micro-texture analysis [12], multi-spectral illumination [250], and user gestures [68,153].…”

Section: Addressing Spoofing Attacksmentioning

confidence: 99%

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Countering digital replay attacks for face verification on consumer smart devices using structured illumination

Smith¹

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This thesis examines countermeasures to digital replay attacks using existing consumer smart device technology. The current literature shows virtually no significant research on practical solutions to these attacks, which limits the ability to leverage such smart devices as biometric readers. Enhancing existing password-based authentication with biometrics has many positive benefits to users. The ultimate goal is that, one day, accessing online accounts will be as simple as looking at your phone.Current password-based authentication is complex, prone to error, and users find it difficult. Replacing passwords with hardware devices removes some of the complexity, but results in an accumulation of more devices (or tokens). Both of these forms of authentication have the same weakness: they only authenticate knowledge of the password, or possession of the token; they do not authenticate the actual person. Anyone with possession of your ATM card and PIN can withdraw money from your account, even though they are not you.Biometric systems promise to provide usable authentication that identifies you as a person. You benefit from additional security, and service providers benefit from being able to show who requested the service.Unfortunately, biometric systems have not experienced wide adoption, partly due to the expense of deploying biometric sensors. The widespread deployment of consumer smart devices now offers the opportunity to place biometric sensor devices in every user's hands with little or no additional expense. For example, the camera interface can capture face images of the user for authentication purposes.However, these devices cannot be fully trusted. Like many systems, they are many points within the system that attackers may attack to their advantage. Users often know of presentation or spoofing attacks due to their portrayal in popular movies. Nevertheless, defeating a face verification system by using a photograph, video, or mask in front of the camera is becoming much more difficult due to significant recent research efforts into anti-spoofing techniques. These techniques aim to distinguish live people from facsimile copies.Instead, if the attacker replaces the digital representation of the user's face with that of a different (but equally real) user's face, the anti-spoofing and liveness testing will all conclude that the digital signal is that of a real person. Thus, anti-spoofing technology completely fails to detect the forgery.These attacks are termed digital replay attacks as they replace the valid digital signal by replaying a previously obtained signal. Digital replay attacks are easy to implement, and may be widely automated using malicious software, such as viruses and trojan horses. Such attack automation uses the smart device to capture valid images of the victim for use elsewhere at a later time. This is in contrast to the spoofing attack, in which the attacker must carefully customise each attack for each intended victim.ii Currently, little practical research determines counterme...

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Avoiding replay-attacks in a face recognition system using head-pose estimation

Cited by 57 publications

References 2 publications

Liveness Detection Using Gaze Collinearity

Liveness Detection Using Gaze Collinearity

Directional Sensitivity of Gaze-Collinearity Features in Liveness Detection

Countering digital replay attacks for face verification on consumer smart devices using structured illumination

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