Sorry, you need to enable JavaScript to visit this website.

PhoneSpoof: A new dataset for spoofing attack detection in telephone channel

Citation Author(s):
Galina Lavrentyeva, Sergey Novoselov, Marina Volkova, Yuri Matveev, Maria De Marsico
Submitted by:
Galina Lavrentyeva
Last updated:
7 May 2019 - 1:13pm
Document Type:
Poster
Document Year:
2019
Event:
Presenters:
Galina Lavrentyeva
Paper Code:
ICASSP19005
Categories:
Keywords:
 

The results of spoofing detection systems proposed during ASVspoof Challenges 2015 and 2017 confirmed the perspective in detection of unforseen spoofing trials in microphone channel. However, telephone channel presents much more challenging conditions for spoofing detection, due to limited bandwidth, various coding standards and channel effects. Research on the topic has thus far only made use of program codecs and other telephone channel emulations. Such emulations does not quite match the real telephone spoofing attacks. In order to asses spoofing detection methods in real scenario we present the PHONESPOOF dataset - spoofing data collected through realistic telephone channels. The PHONE-SPOOF data collection represents most threatening types of spoofing attacks and is publicly available dataset 1 . This work 2 aimed to investigate robustness of the state-of-the-art deep learning based antispoofing systems under telephone spoofing attacks conditions based on the PHONESPOOF data. Moreover newly collected dataset makes it possible to analize language dependency issue for the Anti-Spoofing methods. In the work we also focused on the development of a unified LCNN-based approach for spoofing attack detection. The goal was to train a single system able to detect various types of spoofing attacks in telephone channel. The obtained results approve the effectiveness of such solution.
https://ieeexplore.ieee.org/document/8682942

up
0 users have voted: