Spoofprint: A New Paradigm for Spoofing Attacks Detection

Abstract

With the development of voice spoofing techniques, voice spoofing attacks have become one of the main threats to automatic speaker verification (ASV) systems. Traditionally, researchers tend to treat this problem as a binary classification task. A binary classifier is typically trained using machine learning (including deep learning) algorithms to determine whether a given audio clip is bonafide or spoofed. This approach is effective on detecting spoofing attacks that are generated by known voice spoofing techniques. However, in practical scenarios, new types of spoofing technologies are emerging rapidly. It is impossible to include all types of spoofing technologies into the training dataset, and thus it is desired that the detection system can generalize to unseen spoofing techniques. In this paper, we propose a new paradigm for spoofing attacks detection called Spoofprint. Instead of using a binary classifier to detect spoofed audio, Spoofprint uses a paradigm similar to ASV systems and involves an enrollment phase and a verification phase. We evaluate the performance on the original and noisy versions of ASVspoof 2019 logical access (LA) dataset. The results show that the proposed Spoofprint paradigm is effective on detecting unknown type of attacks and is often superior to the latest state-of-the-art.