Adversarial defense for deep speaker recognition using hybrid adversarial training

Understand

Deep neural network based speaker recognition systems can easily be deceived by an adversary using minuscule imperceptible perturbations to the input speech samples.

• These adversarial attacks pose serious security threats to the speaker recognition systems that use speech biometric.
• To address this concern, in this work, we propose a new defense mechanism based on a hybrid adversarial training (HAT) setup.
• In contrast to existing works on countermeasures against adversarial attacks in deep speaker recognition that only use class-boundary information by supervised cross-entropy (CE) loss, we propose to exploit additional information from supervised and unsupervised cues to craft diverse and stronger perturbations for adversarial training.