Previous databases have been designed to further the development of fake
audio detection. However, fake utterances are mostly generated by altering
timbre, prosody, linguistic content or channel noise of original audios. They
ignore a fake situation, in which the attacker manipulates an acoustic scene of
the original audio with another forgery 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 designs such a
dataset for scene fake audio detection (SceneFake). A manipulated audio in the
SceneFake dataset involves only tampering the acoustic scene of an utterance by
using speech enhancement technologies. We can not only detect fake utterances
on a seen test set but also evaluate the generalization of fake detection
models to unseen manipulation attacks. Some benchmark results are described on
the SceneFake dataset. Besides, an analysis of fake attacks with different
speech enhancement technologies and signal-to-noise ratios are presented on the
dataset. The results show that scene manipulated utterances can not be detected
reliably by the existing baseline models of ASVspoof 2019. Furthermore, the
detection of unseen scene manipulation audio is still challenging.

本研究旨在设计用于检测伪造声音的数据集 SceneFake，其中出现的操纵声音仅涉及使用语音增强技术将发音的声学场景篡改。该数据集不仅可以在已知操纵的测试集上检测假话，还可评估假检测模型对未知操纵攻击的泛化能力，并对使用不同语音增强技术和信噪比实施的伪造攻击进行了分析。结果表明，ASVspoof 2019 的现有基线模型无法可靠地检测到场景被篡改的语音，并且检测未知场景操纵声音仍然具有挑战性。

SceneFake: 场景虚假音频检测的初始数据集和基准

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

A great deal of recent research effort on speech spoofing countermeasures has
been invested into back-end neural networks and training criteria. We
contribute to this effort with a comparative perspective in this study. Our
comparison of countermeasure models on the ASVspoof 2019 logical access task
takes into account recently proposed margin-based training criteria, widely
used front ends, and common strategies to deal with varied-length input trials.
We also measured intra-model differences through multiple training-evaluation
rounds with random initialization. Our statistical analysis demonstrates that
the performance of the same model may be significantly different when just
changing the random initial seed. Thus, we recommend similar analysis or
multiple training-evaluation rounds for further research on the database.
Despite the intra-model differences, we observed a few promising techniques
such as the average pooling to process varied-length inputs and a new
hyper-parameter-free loss function. The two techniques led to the best single
model in our experiment, which achieved an equal error rate of 1.92% and was
significantly different in statistical sense from most of the other
experimental models.

本研究通过对后端神经网络与训练标准的比较，评估 ASVspoof 2019 逻辑访问任务的对策模型，发现随机初始种子会对模型性能造成显著影响，但平均池化和新的无超参数损失函数是比较优秀的技术。

近期神经欺骗对抗合成语音检测方法比较研究

A Comparative Study on Recent Neural Spoofing Countermeasures for  Synthetic Speech Detection

Spoofing countermeasures aim to protect automatic speaker verification
systems from attempts to manipulate their reliability with the use of spoofed
speech signals. While results from the most recent ASVspoof 2019 evaluation
show great potential to detect most forms of attack, some continue to evade
detection. This paper reports the first application of RawNet2 to
anti-spoofing. RawNet2 ingests raw audio and has potential to learn cues that
are not detectable using more traditional countermeasure solutions. We describe
modifications made to the original RawNet2 architecture so that it can be
applied to anti-spoofing. For A17 attacks, our RawNet2 systems results are the
second-best reported, while the fusion of RawNet2 and baseline countermeasures
gives the second-best results reported for the full ASVspoof 2019 logical
access condition. Our results are reproducible with open source software.

本研究是在自动说话者验证中首次应用 RawNet2 以实现反欺骗。我们修改了 RawNet2 架构并与传统反欺骗解决方案混合使用，以检测 ASVspoof 2019 中逻辑访问条件的 A17 攻击。我们的实验结果可以使用开源软件进行复制。