Cutting-edge research on Parrot Training-Audio Embedding (PT-AE) attacks has developed an optimized mechanism for generating attacks against black-box target models in speaker recognition systems.

Various audio carriers like noise, feature-twisted, and environmental sounds were explored, with environmental sound carriers proving to be the most effective for high True Positive Rates (TPRs) in PT-AEs.

A human study dataset demonstrated that environmental sound carriers achieved the best transferability of PT-AEs, slightly less effective than Generative Transfer Adversarial Examples (GT-AEs).

The Transferability-Perception Ratio (TPR) was introduced to measure the joint perspective of transferability and perception, with speech length being crucial for successful PT-AEs.

The parrot training method leverages voice conversion advancements to create a parrot-trained (PT) model, producing PT-AEs with high transferability and good perceptual quality.

Real-world experiments confirmed the effectiveness of the PT-AE attack against open-source models and smart devices.

The proposed PT-AE strategy outperforms existing attack strategies, requiring minimal knowledge from the attacker.

The study presents a novel and practical approach to black-box attacks on speaker recognition systems, highlighting the power of parrot training in generating effective PT-AEs.