Attack Agnostic Dataset: Towards Generalization and Stabilization of Audio DeepFake Detection
Preprint
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%.Keywords
All Related Versions
- Version 1, 2022-06-27, ArXiv
- Version 2, 2022-07-21, ArXiv
- Published version: , 4023.
This publication has 7 references indexed in Scilit:
- End-to-end spectro-temporal graph attention networks for speaker verification anti-spoofing and speech deepfake detection2021
- ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection2021
- A Comparative Study on Recent Neural Spoofing Countermeasures for Synthetic Speech Detection2021
- End-to-End anti-spoofing with RawNet22021
- ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speechComputer Speech & Language, 2020
- ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection2019
- Robust Bayesian and Light Neural Networks for Voice Spoofing Detection2019