Spoofing attacks to i-vector based voice verification systems using statistical speech synthesis with additive noise and countermeasure

Name: Spoofing attacks to i-vector based voice verification systems using statistical speech synthesis with additive noise and countermeasure
Author: Özbay, Mustafa Caner, Khodabakhsh, Ali, Mohammadi, Amir, Demiroğlu, Cenk

İsim	Spoofing attacks to i-vector based voice verification systems using statistical speech synthesis with additive noise and countermeasure
Yazar	Özbay, Mustafa Caner, Khodabakhsh, Ali, Mohammadi, Amir, Demiroğlu, Cenk
Basım Tarihi:	2016
Basım Yeri	- IEEE
Konu	Spoofing attacks, Speaker verification, Statistical speech synthesis, Speaker adaptation, Synthetic speech detection
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2076-1465
Kayıt Numarası	2ad521ab-349e-45c7-aff9-c9c670f0886b
Lokasyon	Electrical & Electronics Engineering
Tarih	2016
Notlar	TÜBİTAK
Örnek Metin	Even though improvements in the speaker verification (SV) technology with i-vectors increased their real-life deployment, their vulnerability to spoofing attacks is a major concern. Here, we investigated the effectiveness of spoofing attacks with statistical speech synthesis systems using limited amount of adaptation data and additive noise. Experiment results show that effective spoofing is possible using limited adaptation data. Moreover, the attacks get substantially more effective when noise is intentionally added to synthetic speech. Training the SV system with matched noise conditions does not alleviate the problem. We propose a synthetic speech detector (SSD) that uses session differences in i-vectors for counterspoofing. The proposed SSD had less than 0.5% total error rate in most cases for the matched noise conditions. For the mismatched noise conditions, missed detection rate further decreased but total error increased which indicates that some calibration is needed for mismatched noise conditions.
DOI	10.1109/EUSIPCO.2016.7760440

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Spoofing attacks to i-vector based voice verification systems using statistical speech synthesis with additive noise and countermeasure

Yazar Özbay, Mustafa Caner, Khodabakhsh, Ali, Mohammadi, Amir, Demiroğlu, Cenk

Basım Tarihi 2016

Basım Yeri - IEEE

Konu Spoofing attacks, Speaker verification, Statistical speech synthesis, Speaker adaptation, Synthetic speech detection

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2076-1465

Kayıt Numarası 2ad521ab-349e-45c7-aff9-c9c670f0886b

Lokasyon Electrical & Electronics Engineering

Tarih 2016

Notlar TÜBİTAK

Örnek Metin Even though improvements in the speaker verification (SV) technology with i-vectors increased their real-life deployment, their vulnerability to spoofing attacks is a major concern. Here, we investigated the effectiveness of spoofing attacks with statistical speech synthesis systems using limited amount of adaptation data and additive noise. Experiment results show that effective spoofing is possible using limited adaptation data. Moreover, the attacks get substantially more effective when noise is intentionally added to synthetic speech. Training the SV system with matched noise conditions does not alleviate the problem. We propose a synthetic speech detector (SSD) that uses session differences in i-vectors for counterspoofing. The proposed SSD had less than 0.5% total error rate in most cases for the matched noise conditions. For the mismatched noise conditions, missed detection rate further decreased but total error increased which indicates that some calibration is needed for mismatched noise conditions.

DOI 10.1109/EUSIPCO.2016.7760440