GANBA: Generative Adversarial Network for Biometric Anti-Spoofing
Metadatos
Mostrar el registro completo del ítemEditorial
MDPI
Materia
Adversarial attacks Automatic speaker verification (ASV) Anti-spoofing Presentation attack detection (PAD) Voice biometrics
Fecha
2022-01-29Referencia bibliográfica
Gomez-Alanis, A.; Gonzalez-Lopez , J.A.; Peinado, A.M. GANBA: Generative Adversarial Network for Biometric Anti-Spoofing. Appl. Sci. 2022, 12, 1454. [https://doi.org/10.3390/app12031454]
Patrocinador
FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades Proyecto PY20_00902; PID2019-104206GB-I00 funded by MCIN/ AEI /10.13039/501100011033Resumen
Automatic speaker verification (ASV) is a voice biometric technology whose security
might be compromised by spoofing attacks. To increase the robustness against spoofing attacks,
presentation attack detection (PAD) or anti-spoofing systems for detecting replay, text-to-speech and
voice conversion-based spoofing attacks are being developed. However, it was recently shown that
adversarial spoofing attacks may seriously fool anti-spoofing systems. Moreover, the robustness of the
whole biometric system (ASV + PAD) against this new type of attack is completely unexplored. In
this work, a new generative adversarial network for biometric anti-spoofing (GANBA) is proposed.
GANBA has a twofold basis: (1) it jointly employs the anti-spoofing and ASV losses to yield very
damaging adversarial spoofing attacks, and (2) it trains the PAD as a discriminator in order to make
them more robust against these types of adversarial attacks. The proposed system is able to generate
adversarial spoofing attacks which can fool the complete voice biometric system. Then, the resulting
PAD discriminators of the proposed GANBA can be used as a defense technique for detecting both
original and adversarial spoofing attacks. The physical access (PA) and logical access (LA) scenarios of
the ASVspoof 2019 database were employed to carry out the experiments. The experimental results
show that the GANBA attacks are quite effective, outperforming other adversarial techniques when
applied in white-box and black-box attack setups. In addition, the resulting PAD discriminators are
more robust against both original and adversarial spoofing attacks.