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ISCApad Archive » 2018 » ISCApad #240 » Events » Other Events » (2018-09-17) DISRUPTIVE RESEARCH IN ANTI-SPOOFING FOR AUTOMATIC SPEAKER VERIFICATION at MLSP 2018, Aalborg, Denmark |
ISCApad #240 |
| Tuesday, June 12, 2018 by Chris Wellekens |
3-3-22 (2018-09-17) DISRUPTIVE RESEARCH IN ANTI-SPOOFING FOR AUTOMATIC SPEAKER VERIFICATION at MLSP 2018, Aalborg, Denmark
CALL FOR PAPERS:
IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2018)
Aalborg, Denmark
Special Session:
DISRUPTIVE RESEARCH IN ANTI-SPOOFING FOR AUTOMATIC SPEAKER VERIFICATION
Research in anti-spoofing for automatic speaker verification has advanced significantly in the last five years. While proposed countermeasures are effective in detecting and deflecting spoofing attacks, current solutions lack a solid grounding in the processes involved in the mounting of spoofing attacks. As a result, and with most current solutions relying on the somewhat blind use of relatively standard features and classifiers, many countermeasures fail
when they encounter different forms of attack and are unlikely to generalise well to attacks encountered in the wild. This special session, organised as part of MLSP 2018, seeks to break the mould in anti-spoofing research. We invite scientific contributions that explore fundamentally disruptive approaches to anti-spoofing for automatic speaker verification. While contributions which use existing standard/common databases are welcome, their use is not required. Preference will instead be given to contributions that explore under-researched aspects of spoofing and non-standard, emerging or blue-sky countermeasure technologies, especially those with an emphasis on previously-unexplored signal processing and machine learning approaches which either shed new light on spoofing or expose promising new research directions for future exploration. Both technological and methodological contributions
are welcome.
Example topics include but are by no means limited to the following:
- theoretical bounds of spoofing attack detectability
- cross-domain feature learning for robust spoofing attack detection
- generative adversarial networks and threats to biometric technology
- one-class, semi-supervised, or reinforcement learning approaches to spoofing countermeasures
- new regularisation and optimisation methods to improve cross-dataset generality
- generation and detection of inaudible, imperceptible or other novel spoofing attacks
- novel hardware/sensor and knowledge-based spoofing countermeasures
- alternatives to GMMs, DNNs, CNNs, RNNs
- unexpected application areas beyond biometrics
Schedule is the same as for regular papers:
Paper submission: May 1 (update until May 4)
Review notifications: June 18
Author rebuttals: June 18-24
Reviewer discussion: June 25-30
Decision notification: July 6
Camera-ready paper & registration: July 31
Organizers:
Nicholas Evans, EURECOM, France (evans@eurecom.fr)
Tomi Kinnunen, University of Eastern Finland, Finland (tkinnu@cs.joensuu.fi)
Sébastien Marcel, IDIAP, Switzerland (sebastien.marcel@idiap.ch)
Zheng-Hua Tan, Aalborg University, Denmark (zt@es.aau.dk)
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