Scalable Ensemble-based Detection Method against Adversarial Attacks for
  speaker verification
Haibin Wu Heng-Cheng Kuo Yu Tsao Hung-yi Lee
Automatic speaker verification (ASV) is highly susceptible to adversarial
attacks. Purification modules are usually adopted as a pre-processing to
mitigate adversarial noise. However, they are commonly implemented across
diverse experimental settings, rendering direct comparisons challenging. This
paper comprehensively compares mainstream purification techniques in a unified
framework. We find these methods often face a trade-off between user experience
and security, as they struggle to simultaneously maintain genuine sample
performance and reduce adversarial perturbations. To address this challenge,
some efforts have extended purification modules to encompass detection
capabilities, aiming to alleviate the trade-off. However, advanced purification
modules will always come into the stage to surpass previous detection method.
As a result, we further propose an easy-to-follow ensemble approach that
integrates advanced purification modules for detection, achieving
state-of-the-art (SOTA) performance in countering adversarial noise. Our
ensemble method has great potential due to its compatibility with future
advanced purification techniques.