This study aims to develop a single integrated spoofing-aware speaker
verification (SASV) embeddings that satisfy two aspects. First, rejecting
non-target speakers' input as well as target speakers' spoofed inputs should be
addressed. Second, competitive performance should be demonstrated compared to
the fusion of automatic speaker verification (ASV) and countermeasure (CM)
embeddings, which outperformed single embedding solutions by a large margin in
the SASV2022 challenge. We analyze that the inferior performance of single SASV
embeddings comes from insufficient amount of training data and distinct nature
of ASV and CM tasks. To this end, we propose a novel framework that includes
multi-stage training and a combination of loss functions. Copy synthesis,
combined with several vocoders, is also exploited to address the lack of
spoofed data. Experimental results show dramatic improvements, achieving a
SASV-EER of 1.06% on the evaluation protocol of the SASV2022 challenge.

本研究旨在开发一种单一的整合了反欺骗技术的演讲者验证嵌入式系统，此系统具备拒绝非目标演讲者及目标演讲者欺诈性输入的能力，而且与融合自动演讲者验证（ASV）及反欺骗对策（CM）嵌入式系统相比，能够具备竞争性的表现。该研究提出的新框架包括多阶段训练与损失函数的组合，并利用多种声码器与合成技术，来回应训练数据不足及短缺欺骗性数据的问题。实验结果表明出色的改善，进而实现了对 SASV2022 挑战赛评估协议的 SASV-EER 低至 1.06% 的性能。

面向单一集成的防欺骗说话人验证嵌入

Towards single integrated spoofing-aware speaker verification embeddings

Automatic speaker verification (ASV) is one of the core technologies in
biometric identification. With the ubiquitous usage of ASV systems in
safety-critical applications, more and more malicious attackers attempt to
launch adversarial attacks at ASV systems. In the midst of the arms race
between attack and defense in ASV, how to effectively improve the robustness of
ASV against adversarial attacks remains an open question. We note that the
self-supervised learning models possess the ability to mitigate superficial
perturbations in the input after pretraining. Hence, with the goal of effective
defense in ASV against adversarial attacks, we propose a standard and
attack-agnostic method based on cascaded self-supervised learning models to
purify the adversarial perturbations. Experimental results demonstrate that the
proposed method achieves effective defense performance and can successfully
counter adversarial attacks in scenarios where attackers may either be aware or
unaware of the self-supervised learning models.

通过使用级联自监督学习模型可在 ASV 中对抗对抗攻击，并提高自身的鲁棒性。实验结果表明，该方法能在攻击者知道或不知道自监督模型的情况下有效地抵御攻击，提高了防御性能。