A Study on Replay Attack and Anti-Spoofing for Automatic Speaker Verification

Li, Lantian; Chen, Yixiang; Wang, Dong; Zheng, Thomas Fang

doi:10.21437/interspeech.2017-456

Cited by 42 publications

(29 citation statements)

References 9 publications

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Section: Related Workmentioning

confidence: 99%

“…For example, artifacts introduced by the acoustic surroundings, such as reverberation, might be confused with the artifacts introduced by playback in some cases. Li et al [9] tried to use machine learning to detect replay attacks, but only obtained poor performance due to overfitting.In the ASVspoof 2017 challenge, an official corpus for detecting replay attack and a baseline system, based on the Gaussian mixture model (GMM) with constant Q cepstral coefficient (CQCC) features [10], were provided. The challenge required participants to propose a method that distinguished between genuine speech and a replay recording, where a total of 49 submissions were received from the participants.…”

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confidence: 99%

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Attention-Based LSTM Algorithm for Audio Replay Detection in Noisy Environments

Zhang

Sun

et al. 2019

Applied Sciences

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Even though audio replay detection has improved in recent years, its performance is known to severely deteriorate with the existence of strong background noises. Given the fact that different frames of an utterance have different impacts on the performance of spoofing detection, this paper introduces attention-based long short-term memory (LSTM) to extract representative frames for spoofing detection in noisy environments. With this attention mechanism, the specific and representative frame-level features will be automatically selected by adjusting their weights in the framework of attention-based LSTM. The experiments, conducted using the ASVspoof 2017 dataset version 2.0, show that the equal error rate (EER) of the proposed approach was about 13% lower than the constant Q cepstral coefficients-Gaussian mixture model (CQCC-GMM) baseline in noisy environments with four different signal-to-noise ratios (SNR). Meanwhile, the proposed algorithm also improved the performance of traditional LSTM on audio replay detection systems in noisy environments. Experiments using bagging with different frame lengths were also conducted to further improve the proposed approach.

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Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

Attention-Based LSTM Algorithm for Audio Replay Detection in Noisy Environments

Zhang

Sun

et al. 2019

Applied Sciences

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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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“…Constant Q cepstral coefficients (CQCC), which is proposed by Todisco M et al [7], was adopted in the baseline system for this challenge. After that, various features were used in recent literature to improve the performance of replay detection, such as the inverted Mel-frequency cepstral coefficients (IMFCC) [8], single frequency filtering coefficients (SFFCC) [9], high-frequency cepstral coefficients (HFCC) [10], and linear frequency cepstral coefficients (LFCC) [11]. All these works used CQCC features as baseline features and a Gaussian Mixture Model (GMM) classifier for the final classification.…”

Section: Introductionmentioning

confidence: 99%

A Robust Method for Speech Replay Attack Detection

2020

KSII TIIS

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Spoofing attacks, especially replay attacks, pose great security challenges to automatic speaker verification (ASV) systems. Current works on replay attacks detection primarily focused on either developing new features or improving classifier performance, ignoring the effects of feature variability, e.g., the channel variability. In this paper, we first establish a mathematical model for replay speech and introduce a method for eliminating the negative interference of the channel. Then a novel feature is proposed to detect the replay attacks. To further boost the detection performance, four post-processing methods using normalization techniques are investigated. We evaluate our proposed method on the ASVspoof 2017 dataset. The experimental results show that our approach outperforms the competing methods in terms of detection accuracy. More interestingly, we find that the proposed normalization strategy could also improve the performance of the existing algorithms.

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A Study on Replay Attack and Anti-Spoofing for Automatic Speaker Verification

Cited by 42 publications

References 9 publications

Attention-Based LSTM Algorithm for Audio Replay Detection in Noisy Environments

Attention-Based LSTM Algorithm for Audio Replay Detection in Noisy Environments

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

A Robust Method for Speech Replay Attack Detection

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