Biometrics Evaluation Under Spoofing Attacks

Chingovska, Ivana; Anjos, André; Marcel, Sébastien

doi:10.1109/tifs.2014.2349158

Cited by 93 publications

(82 citation statements)

References 41 publications

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“…Again, just like the biometric system, it will also make mistakes, leading to Even as independent sub-systems, spoofing countermeasures impact directly on the performance of the biometric system as a whole; while aiming to reduce vulnerabilities to spoofing, they also have potential to reject genuine trials [7]. Thus, while countermeasure sub-systems can be assessed independently, it is often of greater interest to assess the performance of the system as a whole, for example using a score fusion strategy illustrated in Fig.…”

Section: Evaluation Methodology: Spoofing Countermeasuresmentioning

confidence: 99%

Biometrics Systems Under Spoofing Attack: An evaluation methodology and lessons learned

Hadid

Evans²,

Marcel

et al. 2015

IEEE Signal Process. Mag.

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188

103

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Biometrics already form a significant component of current and emerging identification technologies.Biometrics systems aim to determine or verify the identity of an individual from their behavioural and/or biological characteristics. Despite significant progress, some biometric systems fail to meet the multitude of stringent security and robustness requirements to support their deployment in some practical scenarios.Among current concerns are vulnerabilities to spoofing, namely persons who masquerade as others in order to gain illegitimate accesses to protected data, services or facilities. While the study of spoofing, or rather anti-spoofing, has attracted growing interest in recent years, the problem is far from being solved and will require far greater attention in coming years. This tutorial article presents an introduction to spoofing and anti-spoofing research. It describes the vulnerabilities, presents an evaluation methodology for the assessment of spoofing and countermeasures, and outlines research priorities for the future.

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Section: Evaluation Methodology: Spoofing Countermeasuresmentioning

confidence: 99%

Biometrics Systems Under Spoofing Attack: An evaluation methodology and lessons learned

Hadid

Evans²,

Marcel

et al. 2015

IEEE Signal Process. Mag.

Self Cite

188

103

View full text Add to dashboard Cite

show abstract

“…In this paper, we adopt an evaluation methodology specifically designed for performance assessment of fusion system proposed in [25]. …”

Section: Score Fusionmentioning

confidence: 99%

“…Such flexibility can be valuable in practical systems. See [25] for a detailed comparison of the different fusion schemes and their discussion.…”

Section: Asv Systemsmentioning

confidence: 99%

Impact of Score Fusion on Voice Biometrics and Presentation Attack Detection in Cross-Database Evaluations

Korshunov

Marcel

2017

IEEE J. Sel. Top. Signal Process.

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Abstract-Research in the area of automatic speaker verification (ASV) has advanced enough for the industry to start using ASV systems in practical applications. However, these systems are highly vulnerable to spoofing or presentation attacks, limiting their wide deployment. Therefore, it is important to develop mechanisms that can detect such attacks, and it is equally important for these mechanisms to be seamlessly integrated into existing ASV systems for practical and attack-resistant solutions. To be practical, however, an attack detection should have (i) high accuracy, (ii) be well-generalized for different attacks, and (iii) be simple and efficient. Several audio-based presentation attack detection (PAD) methods have been proposed recently but their evaluation was usually done on a single, often obscure, database with limited number of attacks. Therefore, in this paper, we conduct an extensive study of eight state of the art PAD methods and evaluate their ability to detect known and unknown attacks (e.g., in a cross-database scenario) using two major publicly available speaker databases with spoofing attacks: AVspoof and ASVspoof. We investigate whether combining several PAD systems via score fusion can improve attack detection accuracy. We also study the impact of fusing PAD systems (via parallel and cascading schemes) with two i-vector and inter-session variability (ISV)-based ASV systems on the overall performance in both bona fide (no attacks) and spoof scenarios. The evaluation results question the efficiency and practicality of the existing PAD systems, especially when comparing results for individual databases and cross-database data. Fusing several PAD systems can lead to a slightly improved performance, however, how to select which systems to fuse remains an open question. Joint ASV-PAD systems show a significantly increased resistance to the attacks at the expense of slightly degraded performance for bona fide scenarios.

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“…In the upcoming parts of this chapter, we therefore adopt and experimentally apply an evaluation methodology specifically designed for performance assessment of fusion system proposed in [29].…”

Section: Fusionmentioning

confidence: 99%

“…Such flexibility can be valuable in practical systems. See [29] for a detailed comparison of the different fusion schemes and their discussion.…”

Section: Integration Of Pad and Asvmentioning

confidence: 99%

Presentation attack detection in voice biometrics

Korshunov¹,

Marcel²

2017

User-Centric Privacy and Security in Biometrics

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Recent years have shown an increase in both the accuracy of biometric systems and their practical use. The application of biometrics is becoming widespread with fingerprint sensors in smartphones, automatic face recognition in social networks and video-based applications, and speaker recognition in phone banking and other phone-based services. The popularization of the biometric systems, however, exposed their major flaw -high vulnerability to spoofing attacks [1]. A fingerprint sensor can be easily tricked with a simple glue-made mold, a face recognition system can be accessed using a printed photo, and a speaker recognition system can be spoofed with a replay of pre-recorded voice. The ease with which a biometric system can be spoofed demonstrates the importance of developing efficient anti-spoofing systems that can detect both known (conceivable now) and unknown (possible in the future) spoofing attacks.Therefore, it is important to develop mechanisms that can detect such attacks, and it is equally important for these mechanisms to be seamlessly integrated into existing biometric systems for practical and attack-resistant solutions. To be practical, however, an attack detection should have (i) high accuracy, (ii) be well-generalized for different attacks, and (iii) be simple and efficient.One reason for the increasing demand for effective presentation attack detection (PAD) systems is the ease of access to people's biometric data. So often, a potential attacker can almost effortlessly obtain necessary biometric samples from social networks, including facial images, audio and video recordings, and even extract fingerprints from high resolution images. Therefore, various privacy protection solutions, such as legal privacy requirements and algorithms for obfuscating personal information, e.g., visual privacy filters [2], as well as, social awareness of threats to privacy can also increase security of personal information and potentially reduce the vulnerability of biometric systems.In this chapter, however, we focus on presentation attacks detection in voice biometrics, i.e., automatic speaker verification (ASV) systems. We discuss vulnerabilities of these systems to presentation attacks (PAs), present different state of the art 1

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Biometrics Evaluation Under Spoofing Attacks

Cited by 93 publications

References 41 publications

Biometrics Systems Under Spoofing Attack: An evaluation methodology and lessons learned

Biometrics Systems Under Spoofing Attack: An evaluation methodology and lessons learned

Impact of Score Fusion on Voice Biometrics and Presentation Attack Detection in Cross-Database Evaluations

Presentation attack detection in voice biometrics

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