The ASVspoof 2017 Challenge: Assessing the Limits of Replay Spoofing Attack Detection

نویسندگان

  • Tomi Kinnunen
  • Md. Sahidullah
  • Héctor Delgado
  • Massimiliano Todisco
  • Nicholas W. D. Evans
  • Junichi Yamagishi
  • Kong-Aik Lee
چکیده

The ASVspoof initiative was created to promote the development of countermeasures which aim to protect automatic speaker verification (ASV) from spoofing attacks. The first community-led, common evaluation held in 2015 focused on countermeasures for speech synthesis and voice conversion spoofing attacks. Arguably, however, it is replay attacks which pose the greatest threat. Such attacks involve the replay of recordings collected from enrolled speakers in order to provoke false alarms and can be mounted with greater ease using everyday consumer devices. ASVspoof 2017, the second in the series, hence focused on the development of replay attack countermeasures. This paper describes the database, protocols and initial findings. The evaluation entailed highly heterogeneous acoustic recording and replay conditions which increased the equal error rate (EER) of a baseline ASV system from 1.76% to 30.71%. Submissions were received from 49 research teams, 20 of which improved upon a baseline replay spoofing detector EER of 24.65%, in terms of replay/non-replay discrimination. While largely successful, the evaluation indicates that the quest for countermeasures which are resilient in the face of variable replay attacks remains very much alive.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Audio Replay Attack Detection with Deep Learning Frameworks

Nowadays spoofing detection is one of the priority research areas in the field of automatic speaker verification. The success of Automatic Speaker Verification Spoofing and Countermeasures (ASVspoof) Challenge 2015 confirmed the impressive perspective in detection of unforeseen spoofing trials based on speech synthesis and voice conversion techniques. However, there is a small number of researc...

متن کامل

Experimental Analysis of Features for Replay Attack Detection - Results on the ASVspoof 2017 Challenge

This paper presents an experimental comparison of different features for the detection of replay spoofing attacks in Automatic Speaker Verification systems. We evaluate the proposed countermeasures using two recently introduced databases, including the dataset provided for the ASVspoof 2017 challenge. This challenge provides researchers with a common framework for the evaluation of replay attac...

متن کامل

Audio Replay Attack Detection Using High-Frequency Features

This paper presents our contribution to the ASVspoof 2017 Challenge. It addresses a replay spoofing attack against a speaker recognition system by detecting that the analysed signal has passed through multiple analogue-to-digital (AD) conversions. Specifically, we show that most of the cues that enable to detect the replay attacks can be found in the high-frequency band of the replayed recordin...

متن کامل

Ensemble Learning for Countermeasure of Audio Replay Spoofing Attack in ASVspoof2017

To enhance the security and reliability of automatic speaker verification (ASV) systems, ASVspoof 2017 challenge focuses on the detection problem of known and unknown audio replay attacks. We proposed an ensemble learning classifier for CNCB team’s submitted system scores, which across uses a variety of acoustic features and classifiers. An effective postprocessing method is studied to improve ...

متن کامل

SFF Anti-Spoofer: IIIT-H Submission for Automatic Speaker Verification Spoofing and Countermeasures Challenge 2017

The ASVspoof 2017 challenge is about the detection of replayed speech from human speech. The proposed system makes use of the fact that when the speech signals are replayed, they pass through multiple channels as opposed to original recordings. This channel information is typically embedded in low signal to noise ratio regions. A speech signal processing method with high spectro temporal resolu...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2017