Exploration of Compressed ILPR Features for Replay Attack Detection

Jelil, Sarfaraz; Kalita, Sishir; Prasanna, S. R. Mahadeva; Sinha, Rohit

doi:10.21437/interspeech.2018-1297

Cited by 22 publications

(3 citation statements)

References 18 publications

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“…Therefore, developing an effective countermeasure to distinguish between genuine and replayed samples has become a recent research focus [6], [7], [8], [9]. While there have been many prior efforts in this area [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23]), they only focus on detecting replay attacks based on single-channel input and therefore only leverage the temporal and spectral features. However, we identified three reasons why a countermeasure designed using multi-channel audio input could provide improved performance.…”

Section: Introductionmentioning

confidence: 99%

Detecting Replay Attacks Using Multi-Channel Audio: A Neural Network-Based Method

Gong,

Yang,

Poellabauer

2020

Preprint

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With the rapidly growing number of securitysensitive systems that use voice as the primary input, it becomes increasingly important to address these systems' potential vulnerability to replay attacks. Previous efforts to address this concern have focused primarily on single-channel audio. In this paper, we introduce a novel neural network-based replay attack detection model that further leverages spatial information of multi-channel audio and is able to significantly improve the replay attack detection performance.

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Section: Introductionmentioning

confidence: 99%

Detecting Replay Attacks Using Multi-Channel Audio: A Neural Network-Based Method

Gong,

Yang,

Poellabauer

2020

Preprint

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“…The key idea in [9,10] is actually an extension of the antispoofing technologies used for protecting automatic speaker verification (ASV) systems. Many prior efforts (such as presented in [11,12,13,14,15,16,17,18,19,20,21,22]) have attempted to differentiate between original and replayed speech using the RedDots Replayed data set [23]. While the VCS and ASV protection tasks look similar, they have some important differences, e.g., they have fundamental different user scenarios: ASV systems usually assume that the user speaks in a controlled environment and in close proximity to the systems, while modern VCSs usually support far-field speech recognition and are often used in a variety of environmental conditions indoors and outdoors.…”

Section: Introductionmentioning

confidence: 99%

ReMASC: Realistic Replay Attack Corpus for Voice Controlled Systems

Gong

Yang

Huber³

et al. 2019

Interspeech 2019

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This paper introduces a new database of voice recordings with the goal of supporting research on vulnerabilities and protection of voice-controlled systems (VCSs). In contrast to prior efforts, the proposed database contains both genuine voice commands and replayed recordings of such commands, collected in realistic VCSs usage scenarios and using modern voice assistant development kits. Specifically, the database contains recordings from four systems (each with a different microphone array) in a variety of environmental conditions with different forms of background noise and relative positions between speaker and device. To the best of our knowledge, this is the first publicly available database 1 that has been specifically designed for the protection of state-of-the-art voice-controlled systems against various replay attacks in various conditions and environments.

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“…To detect replayed speech from genuine speech, there are several approaches. Some research has focused on tuning the classifier [11][12][13][14], whereas other research has focused on feature extraction [15,16]. As an anti-spoofing task mainly focuses on the characteristics of the given speech, in this paper, we focus on a new feature extraction method that provides better discrimination between replayed speech and genuine speech.…”

Section: Introductionmentioning

confidence: 99%

Replay attack detection with auditory filter-based relative phase features

Wang

Phapatanaburi

et al. 2019

J AUDIO SPEECH MUSIC PROC.

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There are many studies on detecting human speech from artificially generated speech and automatic speaker verification (ASV) that aim to detect and identify whether the given speech belongs to a given speaker. Recent studies demonstrate the success of the relative phase (RP) feature in speaker recognition/verification and the detection of synthesized speech and converted speech. However, there are few studies that focus on the RP feature for replay attack detection. In this paper, we improve the discriminating ability of the RP feature by proposing two new auditory filter-based RP features for replay attack detection. The key idea is to integrate the advantage of RP-based features in signal representation with the advantage of two auditory filter-based RP features. For the first proposed feature, we apply a Mel-filter bank to convert the signal representation of conventional RP information from a linear scale to a Mel scale, where the modified representation is called the Mel-scale RP feature. For the other proposed feature, a gammatone filter bank is applied to scale the RP information, where the scaled RP feature is called the gammatone-scale RP feature. These two proposed phase-based features are implemented to achieve better performance than a conventional RP feature because of the scale resolution and. In addition to the use of individual Mel/gammatone-scale RP features, a combination of the scores of these proposed RP features and a standard magnitude-based feature, that is, the constant Q transform cepstral coefficient (CQCC), is also applied to further improve the reliable detection decision. The effectiveness of the proposed Mel-scale RP feature, gammatone-scale RP feature, and their combination are evaluated using the ASVspoof 2017 dataset. On the evaluation dataset, our proposed methods demonstrate significant improvement over the existing feature and baseline CQCC feature. The combination of the CQCC and gammatone-scale RP provides the best performance compared with an individual baseline feature and other combination methods.

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Exploration of Compressed ILPR Features for Replay Attack Detection

Cited by 22 publications

References 18 publications

Detecting Replay Attacks Using Multi-Channel Audio: A Neural Network-Based Method

Detecting Replay Attacks Using Multi-Channel Audio: A Neural Network-Based Method

ReMASC: Realistic Replay Attack Corpus for Voice Controlled Systems

Replay attack detection with auditory filter-based relative phase features

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