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

Abstract

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). In addition, we propose a model based on LCNN with LFCC and mel-spectrogram front-end, which not only is characterized by a good generalization and stability results but also shows improvement over LFCC-based mode - we decrease standard deviation on all folds and EER in two folds by up to 5%.

arXiv commentProceedings of INTERSPEECH 2022 (Updated version: corrected ASVspoof dataset description)

References in corpus (9)

• Adam: A Method for Stochastic Optimization
• WaveNet: A Generative Model for Raw Audio
• MesoNet: a Compact Facial Video Forgery Detection Network
• HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
• MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
• Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
• ASVspoof 2021: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan
• JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis
• WaveFake: A Data Set to Facilitate Audio Deepfake Detection