Robustness of multimodal biometric fusion methods against spoof attacks

Rodrigues, Ricardo; Ling, Lee Luan; Govindaraju, Venu

doi:10.1016/j.jvlc.2009.01.010

Cited by 130 publications

(199 citation statements)

References 9 publications

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“…However, it has been recently shown [3] that multimodal systems, if naively tunned, are intrinsically less secure than unimodal ones. This suggests each biometric modality needs to be protected by its own specialized counter-measures.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%

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

2014

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“…However, Rodrigues et al [18] showed that such systems are even less secure if one of the biometric traits can be spoofed. Subsequently, Johnson et al [19], Akhtar et al [20], Marasco et al [21] proposed fusion methods more robust against spoofing attempts.…”

Section: Related Workmentioning

confidence: 99%

BiometricJammer: Method to Prevent Acquisition of Biometric Information by Surreptitious Photography on Fingerprints

Echizen

Ogane

2018

IEICE Trans. Inf. & Syst.

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SUMMARYAdvances in fingerprint authentication technology have led to it being used in a growing range of personal devices such as PCs and smartphones. However, they have also made it possible to capture fingerprints remotely with a digital camera, putting the target person at risk of illegal log-ins and identity theft. This article shows how fingerprint captured in this manner can be authenticated and how people can protect their fingerprints against surreptitious photography. First we show that photographed fingerprints have enough information to spoof fingerprint authentication systems by demonstrating with "fake fingers" made from such photographs. Then we present a method that defeats the use of surreptitious photography without preventing the use of legitimate fingerprint authentication devices. Finally, we demonstrate that an implementation of the proposed method called "BiometricJammer," a wearable device put on a fingertip, can effectively prevent the illegal acquisition of fingerprints by surreptitious photography while still enabling contact-based fingerprint sensors to respond normally.

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“…For the purpose of this assessment we created an ensemble of payloads containing artificial anomalies starting from a set of legitimate payloads. Artificial anomalies have been employed both for training and testing purposes in Intrusion Detection as well as in Spam Detection and Biometrics [45,46,47].…”

Section: Sensitivity To Anomaliesmentioning

confidence: 99%

HMMPayl: An intrusion detection system based on Hidden Markov Models

Ariu

Tronci

Giacinto

2011

Computers & Security

116

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Nowadays the security of Web applications is one of the key topics in Computer Security. Among all the solutions that have been proposed so far, the analysis of the HTTP payload at the byte level has proven to be effective as it does not require the detailed knowledge of the applications running on the Web server. The solutions proposed in the literature actually achieved good results for the detection rate, while there is still room for reducing the false positive rate.To this end, in this paper we propose HMMPayl, an IDS where the payload is represented as a sequence of bytes, and the analysis is performed using Hidden Markov Models (HMM). The algorithm we propose for feature extraction and the joint use of HMM guarantee the same expressive power of n−gram analysis, while allowing to overcome its computational complexity. In addition, we designed HMMPayl following the Multiple Classifiers System paradigm to provide for a better classification accuracy, to increase the difficulty of evading the IDS, and to mitigate the weaknesses due to a non optimal choice of HMM parameters. Experimental results, obtained both on public and private datasets, show that the analysis performed by HMMPayl is particularly effective against the most frequent attacks toward Web applications (such as XSS and SQL-Injection). In particular, for a fixed false positive rate, HMMPayl achieves a higher detection rate respect to previously proposed approaches it has been compared with.

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Robustness of multimodal biometric fusion methods against spoof attacks

Cited by 130 publications

References 9 publications

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

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

BiometricJammer: Method to Prevent Acquisition of Biometric Information by Surreptitious Photography on Fingerprints

HMMPayl: An intrusion detection system based on Hidden Markov Models

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