8 research outputs found
GANBA: Generative Adversarial Network for Biometric Anti-Spoofing
Acknowledgments: Alejandro Gomez-Alanis holds a FPU fellowship (FPU16/05490) from the
Spanish Ministry of Education and Vocational Training. Jose A. Gonzalez-Lopez also holds a Juan
de la Cierva-Incorporaci贸n fellowship (IJCI-2017-32926) from the Spanish Ministry of Science and
Innovation. Furthermore, we acknowledge the support of Nvidia with the donation of a Titan X GPU.Data Availability Statement: The ASVspoof 2019 datasets were used in this study. They are publicly
available at https://datashare.ed.ac.uk/handle/10283/3336 (accessed on 5 December 2021).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.FEDER/Junta de Andaluc铆a-Consejer铆a de Transformaci贸n
Econ贸mica, Industria, Conocimiento y Universidades Proyecto PY20_00902PID2019-104206GB-I00 funded by MCIN/ AEI /10.13039/50110001103