评估反欺骗通用对策的范围

Rohan Kumar Das, Jichen Yang, Haizhou Li
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引用次数: 32

摘要

大多数关于反欺骗对策的研究都是针对某种类型的欺骗攻击,其中模型是根据特定性质的数据进行训练的,要么是合成的,要么是重放的。但是,由于在实践中没有关于这种欺骗攻击的先验知识,因此没有这样的杠杆作用。因此,有必要评估反欺骗的广义对策的范围。ASVspoof 2019挑战既包括合成攻击也包括重放攻击,这使得该数据库适合此类研究。在这项工作中,我们考虑了广泛流行的常q倒谱系数特征以及另外两个有前途的前端,它们捕获长期信号特征,以评估它们作为广义对策的范围。此外,对不同版本的ASVspoof语料库进行了全面的研究,以强调在未知条件下稳健的广义对策的必要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing the Scope of Generalized Countermeasures for Anti-Spoofing
Most of the research on anti-spoofing countermeasures are specific to a type of spoofing attacks, where models are trained on data of a particular nature, either synthetic or replay. However, one does not have such leverage as there is no prior knowledge about the kind of spoofing attack in practice. Therefore, there is a requirement to assess the scope of generalized countermeasures for anti-spoofing. The ASVspoof 2019challengecoversboth synthetic as well as replay attacks, which makes the database suitable for such study. In this work, we consider widely popular constant-Q cepstral coefficient features along with two other promising front-ends that capture long-term signal characteristics to assess their scope as generalized countermeasures. Additionally, a comprehensive study is made across different editions of ASVspoof corpora to highlight the need of robust generalized countermeasures in unseen conditions.
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