logo EDITE Federico ALEGRE
Identité
Federico ALEGRE
État académique
Thèse soutenue le 2014-12-15
Sujet: Spoofing protection for biometric speaker recognition
Direction de thèse:
Laboratoire:
Voisinage
Ellipse bleue: doctorant, ellipse jaune: docteur, rectangle vert: permanent, rectangle jaune: HDR. Trait vert: encadrant de thèse, trait bleu: directeur de thèse, pointillé: jury d'évaluation à mi-parcours ou jury de thèse.
Productions scientifiques
oai:hal.archives-ouvertes.fr:hal-00783812
ON THE VULNERABILITY OF AUTOMATIC SPEAKER RECOGNITION TO SPOOFING ATTACKS WITH ARTIFICIAL SIGNALS
Automatic speaker verification (ASV) systems are increasingly being used for biometric authentication even if their vulnerability to imposture or spoofing is now widely acknowledged. Recent work has proposed different spoofing approaches which can be used to test vulnerabilities. This paper introduces a new approach based on artificial, tone-like signals which provoke higher ASV scores than genuine client tests. Experimental results show degradations in the equal error rate from 8.5% to 77.3% and from 4.8% to 64.3% for standard Gaussian mixture model and factor analysis based ASV systems respectively. These findings demonstrate the importance of efforts to develop dedicated countermeasures, some of them trivial, to protect ASV systems from spoofing.
EUSIPCO 2012, 20th European Signal Processing Conferenceinvited conference talk 2012-08-27
oai:hal.archives-ouvertes.fr:hal-00783789
Spoofing countermeasures for the protection of automatic speaker recognition systems against attacks with artificial signals
The vulnerability of automatic speaker recognition systems to imposture or spoofing is widely acknowledged. This paper shows that extremely high false alarm rates can be provoked by simple spoofing attacks with artificial, non-speech-like signals and highlights the need for spoofing countermeasures. We show that two new, but trivial countermeasures based on higher-level, dynamic features and voice quality assessment offer varying degrees of protection and that further work is needed to develop more robust spoofing countermeasure mechanisms. Finally, we show that certain classifiers are inherently more robust to such attacks than others which strengthens the case for fused-system approaches to automatic speaker recognition.
INTERSPEECH 2012, 13th Annual Conference of the International Speech Communication Associationinvited conference talk 2012-09-09
oai:hal.archives-ouvertes.fr:hal-00804543
SPOOFING COUNTERMEASURES TO PROTECT AUTOMATIC SPEAKER VERIFICATION FROM VOICE CONVERSION
This paper presents a new countermeasure for the protection of automatic speaker verification systems from spoofed, converted voice signals. The new countermeasure exploits the common shift applied to the spectral slope of consecutive speech frames involved in the mapping of a spoofer's voice signal towards a statistical model of a given target. While the countermeasure exploits prior knowledge of the attack in an admittedly unrealistic sense, it is shown to detect almost all spoofed signals which otherwise provoke significant increases in false acceptance. The work also discusses the need for formal evaluations to develop new countermeasures which are less reliant on prior knowledge.
ICASSP 2013 (@icassp13). 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing. Vancouver, Canada. ICASSP 2013, 38th IEEE International Conference on Acoustics, Speech, and Signal Processing.conference proceeding 2013-05-31
oai:hal.archives-ouvertes.fr:hal-00849138
A new speaker verification spoofing countermeasure based on local binary patterns
This paper presents a new countermeasure for the protection of automatic speaker verification systems from spoofed, converted voice signals. The new countermeasure is based on the analysis of a sequence of acoustic feature vectors using Local Binary Patterns (LBPs). Compared to existing approaches the new countermeasure is less reliant on prior knowledge and affords robust protection from not only voice conversion, for which it is optimised, but also spoofing attacks from speech synthesis and artificial signals, all of which otherwise provoke significant increases in false acceptance. The work highlights the difficulty in detecting converted voice and also discusses the need for formal evaluations to develop new countermeasures which are less reliant on prior knowledge and thus more reflective of practical use cases.
INTERSPEECH 2013, 14th Annual Conference of the International Speech Communication Association, Lyon: France (2013)article in peer-reviewed journal 2013-08-27
Soutenance
Thèse: "La protection des systèmes de reconnaissance de locuteur contre le leurrage"
Soutenance: 2014-12-15