SceneFake: An Initial Dataset and Benchmarks for Scene Fake Audio Detection

TL;DR

This paper introduces SceneFake, a new dataset for detecting fake audio where the acoustic scene is manipulated, revealing the limitations of current models and emphasizing the need for specialized detection methods.

Contribution

The paper presents SceneFake, the first dataset focused on scene fake audio detection, along with benchmark results and analysis of attack methods using speech enhancement technologies.

Findings

Baseline models perform poorly on unseen test data.

Models trained on ASVspoof 2019 do not generalize well to scene fake audio.

SceneFake dataset exposes gaps in current fake audio detection capabilities.

Abstract

Many datasets have been designed to further the development of fake audio detection. However, fake utterances in previous datasets are mostly generated by altering timbre, prosody, linguistic content or channel noise of original audio. These datasets leave out a scenario, in which the acoustic scene of an original audio is manipulated with a forged one. It will pose a major threat to our society if some people misuse the manipulated audio with malicious purpose. Therefore, this motivates us to fill in the gap. This paper proposes such a dataset for scene fake audio detection named SceneFake, where a manipulated audio is generated by only tampering with the acoustic scene of an real utterance by using speech enhancement technologies. Some scene fake audio detection benchmark results on the SceneFake dataset are reported in this paper. In addition, an analysis of fake attacks with different speech enhancement technologies and signal-to-noise ratios are presented in this paper. The results indicate that scene fake utterances cannot be reliably detected by baseline models trained on the ASVspoof 2019 dataset. Although these models perform well on the SceneFake training set and seen testing set, their performance is poor on the unseen test set. The dataset (https://zenodo.org/record/7663324#.Y_XKMuPYuUk) and benchmark source codes (https://github.com/ADDchallenge/SceneFake) are publicly available.