Robustness of multi-modal biometric verification systems under realistic spoofing attacks

Biggio, Battista; Akthar, Zahid; Fumera, Giorgio; Marcialis, Gian Luca; Roli, Fabio

doi:10.1109/ijcb.2011.6117474

Cited by 17 publications

(17 citation statements)

References 13 publications

(36 reference statements)

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“…Similar results were obtained under the same assumption in [14], [15]. Subsequently, we carried out an extensive experimental analysis on multibiometric systems combining face and fingerprint, to evaluate whether and to what extent this assumption was representative of current techniques for fabricating fake traits (from now on, fake fabrication techniques) [17]- [19]. 3 To this end, we considered different acquisition sensors and matchers, and several presentation attacks fabricated with different techniques.…”

Section: Where We Stand Todaymentioning

confidence: 65%

“…Under the same assumption, more secure fusion rules against presentation attacks have also been proposed. In our recent work, through an extensive experimental analysis, we have shown that the aforementioned assumption is not representative of current face and fingerprint presentation attacks [17]- [19]. In fact, their fake score distributions do not only rarely match those of genuine users, but they can also be very different, depending on the technique, materials, and source images used to fabricate the presentation attack; i.e., presentation attacks can have a different impact on the output of the targeted matcher.…”

Section: Introductionmentioning

confidence: 99%

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Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems

Biggio

Fumera

Marcialis

et al. 2017

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-Prior work has shown that multibiometric systems are vulnerable to presentation attacks, assuming that their matching score distribution is identical to that of genuine users, without fabricating any fake trait. We have recently shown that this assumption is not representative of current fingerprint and face presentation attacks, leading one to overestimate the vulnerability of multibiometric systems, and to design less effective fusion rules. In this paper, we overcome these limitations by proposing a statistical meta-model of face and fingerprint presentation attacks that characterizes a wider family of fake score distributions, including distributions of known and, potentially, unknown attacks. This allows us to perform a thorough security evaluation of multibiometric systems against presentation attacks, quantifying how their vulnerability may vary also under attacks that are different from those considered during design, through an uncertainty analysis. We empirically show that our approach can reliably predict the performance of multibiometric systems even under never-before-seen face and fingerprint presentation attacks, and that the secure fusion rules designed using our approach can exhibit an improved trade-off between the performance in the absence and in the presence of attack. We finally argue that our method can be extended to other biometrics besides faces and fingerprints.

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Section: Where We Stand Todaymentioning

confidence: 65%

Section: Introductionmentioning

confidence: 99%

Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems

Biggio

Fumera

Marcialis

et al. 2017

IEEE Trans. Pattern Anal. Mach. Intell.

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“…Finally, recent works have been developed in order to evaluate spoofing attacks in multi-modal biometric systems including [24]- [28]. In these works, the authors investigate robust fusion schemes for spoofing attacks considering face and fingerprint biometric traits.…”

Section: Other Approachesmentioning

confidence: 99%

Using Visual Rhythms for Detecting Video-Based Facial Spoof Attacks

Pinto

Schwartz

Pedrini

et al. 2015

IEEE Trans.Inform.Forensic Secur.

116

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Spoofing attacks or impersonation can be easily accomplished in a facial biometric system wherein users without access privileges attempt to authenticate themselves as valid users, in which an impostor needs only a photograph or a video with facial information of a legitimate user. Even with recent advances in biometrics, information forensics and security, vulnerability of facial biometric systems against spoofing attacks is still an open problem. Even though several methods have been proposed for photo-based spoofing attack detection, attacks performed with videos have been vastly overlooked, which hinders the use of the facial biometric systems in modern applications. In this paper, we present an algorithm for video-based spoofing attack detection through the analysis of global information which is invariant to content, since we discard video contents and analyze contentindependent noise signatures present in the video related to the unique acquisition processes. Our approach takes advantage of noise signatures generated by the recaptured video to distinguish between fake and valid access videos. For that, we use the Fourier spectrum followed by the computation of video visual rhythms and the extraction of different characterization methods. For evaluation, we consider the novel Unicamp Video-Attack Database (UVAD) which comprises 17,076 videos composed of real access and spoofing attack videos. In addition, we evaluate the proposed method using the Replay-Attack Database, which contains photo-based and video-based face spoofing attacks.

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“…A new method of personal authentication using face and palm print images in [8]. The proposed bimodal system authentication utilized a neural network for obtaining the matching score between the two biometric traits before performing fusion scores.…”

Section: A Related Researchmentioning

confidence: 99%

“…This matrix of eigenvalues is Eigen space V. The data matrix X is projected onto the Eigen space to get P consisting n columns as in equation (8). where…”

Section: Hierarchical Structural Check Matching Algorithmmentioning

confidence: 99%

Hybridization of Bimodal Biometrics for Access Control Authentication

Zuva¹

2014

IJFCC

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Abstract-Single biometric trait for authentication is widely used in some application areas where security is of high importance. However, biometric systems are susceptible to noise, intra-class variation, non-universality and spoof attacks. Thus, there is need to use algorithms that overcome all these limitations found in biometric systems. The use of multimodal biometrics can improve the performance of authentication system. This study proposed using both fingerprint and face for authentication in access system. The study integrated fingerprint and face biometric to improve the performance in access control system. Fingerprint biometric, this paper considered restoration of distorted and misaligned fingerprints caused by environmental noise such as oil, wrinkles, dry skin, dirt, displacement etc. The noisy, distorted and/or misaligned fingerprint produced as a 2-D on x-y image, is enhanced and optimized using a hybrid Modified Gabor Filter-Hierarchal Structure Check (MGF-HSC) system model. In face biometric Fast Principal Component Analysis (FPCA) algorithm was used in which different face conditions (face distortions) such as lighting, blurriness, pose, head orientation and other conditions are addressed. The algorithms used improved the quality of distorted and misaligned fingerprint image. They also improved the recognition accuracy of distorted face during authentication. The results obtained showed that the combination of both fingerprint and face improve the overall performance of biometric authentication system in access control.

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Robustness of multi-modal biometric verification systems under realistic spoofing attacks

Cited by 17 publications

References 13 publications

Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems

Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems

Using Visual Rhythms for Detecting Video-Based Facial Spoof Attacks

Hybridization of Bimodal Biometrics for Access Control Authentication

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