Enhancing cross-domain transferability of black-box adversarial attacks on speaker recognition systems using linearized backpropagation

Speaker recognition system (SRS) serves as the gatekeeper for secure access, using the unique vocal characteristics of individuals for identification and verification. SRS can be found several biometric security applications such as in banks, autonomous cars, military, and smart devices. However, as technology advances, so do the threats to these models. With the rise of adversarial attacks, these models have been put to the test. Adversarial machine learning (AML) techniques have been utilized to exploit vulnerabilities in SRS, threatening their reliability and security. In this study, we concentrate on transferability in AML within the realm of SRS. Transferability refers to the capability of adversarial examples generated for one model to outsmart another model. Our research centers on enhancing the transferability of adversarial attacks in SRS. Our innovative approach involves strategically skipping non-linear activation functions during the backpropagation process to achieve this goal. The proposed method yields promising results in enhancing the transferability of adversarial examples across diverse SRS architectures, parameters, features, and datasets. To validate the effectiveness of our proposed method, we conduct an evaluation using the state-of-the-art FoolHD attack, an attack designed specifically for exploiting SRS. By implementing our method in various scenarios, including cross-architecture, cross-parameter, cross-feature, and cross-dataset settings, we demonstrate its resilience and versatility. To evaluate the performance of the proposed method in improving transferability, we have introduced three novel metrics: enhanced transferability, relative transferability, and effort in enhancing transferability. Our experiments demonstrate a significant boost in the transferability of adversarial examples in SRS. This research contributes to the growing body of knowledge on AML for SRS and emphasizes the urgency of developing robust defenses to safeguard these critical biometric systems.