Replay attack detection based on distortion by loudspeaker for voice authentication

Ren, Yi; Fang, Zhong; Liu, Dengkai; Chen, Chang Wen

doi:10.1007/s11042-018-6834-3

Cited by 19 publications

(9 citation statements)

References 10 publications

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“…In addition to the aforementioned physical biometric traits, researchers have also studied the use of voice for user identification. The results demonstrated that an EER of 11.1% [12], true positive rate of 99.79%, and true negative rate of 99.75% [13] could be achieved. However, voice quality is affected by various user activities, such as running, or due to physical conditions such as sickness.…”

Section: Related Workmentioning

confidence: 99%

“…Biometric recognition is a promising approach and has been accepted in academia and widely used in the industry. For example, many mobile devices now support user authorization with various biometric modalities, such as fingerprint scanning and face, voice, or iris recognition [3][4][5][6][7][8][9][10][11][12][13]. These modalities are examples of physiological biometrics; that is, user identification is based on the distinctive physical characteristics of a user obtained by using specialized hardware sensors.…”

Section: Introductionmentioning

confidence: 99%

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Framework for user behavioural biometric identification using a multimodal scheme with keystroke trajectory feature and recurrent neural network on a mobile platform

Tse¹,

Hung²

2022

IET Biometrics

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Diverse applications are used on mobile devices. Because of the increasing dependence on information systems, immense amounts of personal and sensitive data are stored on mobile devices. Thus, security or privacy breaches are a major challenge. To protect mobile systems and the private information on these systems from being accessed by adversaries, a framework for mobile user identification through the use of a multimodal behavioural biometrics scheme with a keystroke trajectory feature is presented herein. Conventionally, mobile devices have been protected by mechanisms such as PINs or passwords. However, these approaches have numerous disadvantages. Therefore, approaches that employ keystroke biometrics for secure and reliable mobile device identification have been proposed. Because unimodal behavioural biometrics identification mechanisms have limited accuracy and effectiveness, a multimodal scheme that includes different behavioural biometric traits, such as keystroke and swipe biometric traits, is examined. However, the information provided by the spatial and temporal features of keystroke biometrics is not comprehensive. Therefore, a trajectory model is derived to describe the behavioural biometric uniqueness of a user. In the user identification phase, a multistream recurrent neural network (RNN) is adopted. The results reveal that the proposed trajectory model performs well, and the multimodal scheme using an RNN with a late fusion method provides accurate identification results. The proposed system achieved an accuracy of 95.29%, F1 score of 94.64%, and equal error rate of 1.78%. Thus, the proposed mobile identification system is capable of resisting attacks that standard mechanisms may be vulnerable to and represents a valuable contribution to cyber security.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Framework for user behavioural biometric identification using a multimodal scheme with keystroke trajectory feature and recurrent neural network on a mobile platform

Tse¹,

Hung²

2022

IET Biometrics

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“…On the other hand, based on the textual contents of the speech data, voice recognition systems can be classified into two categories; Text-dependent, the identity claimer is expected to produce the same words as those pronounced during the enrollment; in this method, the speaker has to satisfy two conditions, knowing the word and being the rightful owner of the voice [2]. Textindependent, the user can speak freely during enrollment and verification phases [21].…”

Section: Text-dependentmentioning

confidence: 99%

“…The two steps are discussed in Section 2. Due to the portability, stability, and privacy of the voice features, voice recognition authentication has attracted extensive attention and application in recent years [2]. Voice recognition systems are versatile, simple to use, and non-intrusive by nature.…”

Section: Introductionmentioning

confidence: 99%

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Untitled

2020

IJSPTM

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Behavioral biometric authentication is considered as a promising approach to securing the internet of things (IoT) ecosystem. In this paper, we investigated the need and suitability of employing voice recognition systems in the user authentication of the IoT. Tools and techniques used in accomplishing voice recognition systems are reviewed, and their appropriateness to the IoT environment are discussed. In the end, a voice recognition system is proposed for IoT ecosystem user authentication. The proposed system has two phases. The first being the enrollment phase consisting of a pre-processing step where the noise is removed from the voice for the enrollment process, the feature extraction step where feature traits are extracted from user's voice, and the model training step where the voice model is trained for the IoT user. And the second being the phase verifies whether the identity claimer is the owner of the IoT device. Based on the resources limitedness of the IoT technologies, the suitability of text-dependent voice recognition systems is promoted. Likewise, the use of MFCC features is considered in the proposed system.

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