Attack Agnostic Dataset: Towards Generalization and Stabilization of Audio DeepFake Detection

Understand

Audio DeepFakes allow the creation of high-quality, convincing utterances and therefore pose a threat due to its potential applications such as impersonation or fake news.

• Methods for detecting these manipulations should be characterized by good generalization and stability leading to robustness against attacks conducted with techniques that are not explicitly included in the training.
• In this work, we introduce Attack Agnostic Dataset - a combination of two audio DeepFakes and one anti-spoofing datasets that, thanks to the disjoint use of attacks, can lead to better generalization of detection methods.
• We present a thorough analysis of current DeepFake detection methods and consider different audio features (front-ends).