Scalable Ensemble-based Detection Method against Adversarial Attacks for speaker verification (2312.08622v1)

Abstract: 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.

• The paper proposes a scalable ensemble method that integrates purification modules to enhance both detection and mitigation of adversarial noise.
• It overcomes limitations of traditional approaches by balancing security and performance of genuine samples in speaker verification systems.
• The framework’s adaptability positions it as a promising solution for evolving adversarial attack strategies in modern ASV technologies.

The paper "Scalable Ensemble-Based Detection Method Against Adversarial Attacks For Speaker Verification" addresses the vulnerability of Automatic Speaker Verification (ASV) systems to adversarial attacks and proposes a novel solution to enhance their robustness.

ASV systems are often at risk of adversarial attacks, where slight perturbations can deceive the verification process. Traditionally, purification modules have been employed as a preprocessing step to mitigate this adversarial noise. However, these modules can be limited by their need for diverse experimental settings, which complicates direct comparisons and poses a trade-off between user experience and security. Specifically, these methods often struggle to balance maintaining the performance of genuine samples while minimizing adversarial perturbations.

The paper proposes a scalable ensemble method that extends the capabilities of purification modules to include detection, thereby addressing the trade-offs encountered with traditional techniques. By integrating advanced purification modules into an ensemble framework, the approach achieves state-of-the-art performance in detecting and countering adversarial noise. This ensemble method is noted for its potential compatibility with future advancements in purification technologies, suggesting a robust adaptability to emerging threats.

In summary, the research contributes a comprehensive evaluation of existing purification methods within a unified framework and advances the field by offering an ensemble-based approach that enhances both detection and noise mitigation in ASV systems, paving the way for more secure and effective speaker verification.