Audio Replay Attack Detection Using High-Frequency Features

Witkowski, Marcin; Kacprzak, Stanisław; Żelasko, Piotr; Kowalczyk, Konrad; Gałka, Jakub

doi:10.21437/interspeech.2017-776

Cited by 128 publications

(71 citation statements)

References 24 publications

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“…To sum up, previous studies indicate that certain frequency subbands are potentially more informative to the detection of spoofing attacks, even though no standardized approach how that unevenly distributed information across the frequency axis should be utilized. Our current work is different from prior works [19,20,21,22,23] because most of them aim at handcrafting or learning features [24] based on the relevance of specific subbands for spoofing detection. To the best of our knowledge, there is no work in spoofing detection aiming to learn band-specific features by discriminatively training CNNs on a spectrogram input.…”

Section: Relation To Prior Workmentioning

confidence: 99%

Ensemble Models for Spoofing Detection in Automatic Speaker Verification

et al. 2019

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Spectrograms -time-frequency representations of audio signals -have found widespread use in neural network-based spoofing detection. While deep models are trained on the fullband spectrum of the signal, we argue that not all frequency bands are useful for these tasks. In this paper, we systematically investigate the impact of different subbands and their importance on replay spoofing detection on two benchmark datasets: ASVspoof 2017 v2.0 and ASVspoof 2019 PA. We propose a joint subband modelling framework that employs n different sub-networks to learn subband specific features. These are later combined and passed to a classifier and the whole network weights are updated during training. Our findings on the ASVspoof 2017 dataset suggest that the most discriminative information appears to be in the first and the last 1 kHz frequency bands, and the joint model trained on these two subbands shows the best performance outperforming the baselines by a large margin. However, these findings do not generalise on the ASVspoof 2019 PA dataset. This suggests that the datasets available for training these models do not reflect real world replay conditions suggesting a need for careful design of datasets for training replay spoofing countermeasures.

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Section: Relation To Prior Workmentioning

confidence: 99%

Ensemble Models for Spoofing Detection in Automatic Speaker Verification

et al. 2019

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“…At such distances, some acoustic features can be used to identify the sound source of the speaker, e.g., in [31], [32], the authors use the "pop noise" caused by breathing to identify a live speaker. Other efforts [33], [34], [35] do not explicitly use close distance features, but the databases they use to develop their defense strategies were recorded at In the recording phase, the attacker records or synthesizes a malicious voice command. In the playback phase, the malicious voice command is transmitted from the playback device to the victim device over the air.…”

Section: B Sound Source Identification Using Acoustic Cuesmentioning

confidence: 99%

Protecting Voice Controlled Systems Using Sound Source Identification Based on Acoustic Cues

Gong¹,

Poellabauer²

2018

2018 27th International Conference on Computer Communication and Networks (ICCCN)

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Over the last few years, a rapidly increasing number of Internet-of-Things (IoT) systems that adopt voice as the primary user input have emerged. These systems have been shown to be vulnerable to various types of voice spoofing attacks. Existing defense techniques can usually only protect from a specific type of attack or require an additional authentication step that involves another device. Such defense strategies are either not strong enough or lower the usability of the system. Based on the fact that legitimate voice commands should only come from humans rather than a playback device, we propose a novel defense strategy that is able to detect the sound source of a voice command based on its acoustic features. The proposed defense strategy does not require any information other than the voice command itself and can protect a system from multiple types of spoofing attacks. Our proof-of-concept experiments verify the feasibility and effectiveness of this defense strategy.

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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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Audio Replay Attack Detection Using High-Frequency Features

Cited by 128 publications

References 24 publications

Ensemble Models for Spoofing Detection in Automatic Speaker Verification

Ensemble Models for Spoofing Detection in Automatic Speaker Verification

Protecting Voice Controlled Systems Using Sound Source Identification Based on Acoustic Cues

ReMASC: Realistic Replay Attack Corpus for Voice Controlled Systems

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