论文标题
使用子带分类器的非线性融合欺骗攻击检测
Spoofing Attack Detection using the Non-linear Fusion of Sub-band Classifiers
论文作者
论文摘要
欺骗的威胁可能会对自动扬声器验证的可靠性构成风险。两年一次的ASVSPOOF评估结果表明，有效的对策需要专门针对欺骗手工艺的前端。鉴于欺骗攻击的多样性，合奏方法特别有效。本文中的工作表明，一组非常简单的分类器，每个分类器都有一个前端调整，以调节不同的欺骗攻击，并通过非线性融合在得分水平上合并，比依赖复杂的神经网络体系结构的更复杂的集合解决方案可以提供更高的性能。我们相对简单的方法的表现优于除了48个系统中的2个以外的所有方法，该系统提交给了最新的ASVSPOOF 2019挑战的逻辑访问条件。
The threat of spoofing can pose a risk to the reliability of automatic speaker verification. Results from the bi-annual ASVspoof evaluations show that effective countermeasures demand front-ends designed specifically for the detection of spoofing artefacts. Given the diversity in spoofing attacks, ensemble methods are particularly effective. The work in this paper shows that a bank of very simple classifiers, each with a front-end tuned to the detection of different spoofing attacks and combined at the score level through non-linear fusion, can deliver superior performance than more sophisticated ensemble solutions that rely upon complex neural network architectures. Our comparatively simple approach outperforms all but 2 of the 48 systems submitted to the logical access condition of the most recent ASVspoof 2019 challenge.
