Assessing the Scope of Generalized Countermeasures for Anti-Spoofing
Rohan Kumar Das, Jichen Yang, Haizhou Li
Abstract
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 2019 challenge covers both 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.