Generalization is a main issue for current audio deepfake detectors, which
struggle to provide reliable results on out-of-distribution data. Given the
speed at which more and more accurate synthesis methods are developed, it is
very important to design techniques that work well also on data they were not
trained for.In this paper we study the potential of large-scale pre-trained
models for audio deepfake detection, with special focus on generalization
ability. To this end, the detection problem is reformulated in a speaker
verification framework and fake audios are exposed by the mismatch between the
voice sample under test and the voice of the claimed identity. With this
paradigm, no fake speech sample is necessary in training, cutting off any link
with the generation method at the root, and ensuring full generalization
ability. Features are extracted by general-purpose large pre-trained models,
with no need for training or fine-tuning on specific fake detection or speaker
verification datasets. At detection time only a limited set of voice fragments
of the identity under test is required. Experiments on several datasets
widespread in the community show that detectors based on pre-trained models
achieve excellent performance and show strong generalization ability, rivaling
supervised methods on in-distribution data and largely overcoming them on
out-of-distribution data.

通过使用大规模预训练模型，将音频深度伪造检测问题转化为说话人验证框架，利用被测声音样本和所声称身份的声音间的不匹配暴露假音频，实现了对广义能力的全面泛化。实验表明，基于预训练模型的检测器在内部数据上与监督方法不相上下，在外部数据上则大幅超越监督方法。

利用大规模预训练模型进行无需训练的深度伪造语音识别

Training-Free Deepfake Voice Recognition by Leveraging Large-Scale  Pre-Trained Models

The state-of-the-art audio deepfake detectors leveraging deep neural networks
exhibit impressive recognition performance. Nonetheless, this advantage is
accompanied by a significant carbon footprint. This is mainly due to the use of
high-performance computing with accelerators and high training time. Studies
show that average deep NLP model produces around 626k lbs of
CO\textsubscript{2} which is equivalent to five times of average US car
emission at its lifetime. This is certainly a massive threat to the
environment. To tackle this challenge, this study presents a novel framework
for audio deepfake detection that can be seamlessly trained using standard CPU
resources. Our proposed framework utilizes off-the-shelve self-supervised
learning (SSL) based models which are pre-trained and available in public
repositories. In contrast to existing methods that fine-tune SSL models and
employ additional deep neural networks for downstream tasks, we exploit
classical machine learning algorithms such as logistic regression and shallow
neural networks using the SSL embeddings extracted using the pre-trained model.
Our approach shows competitive results compared to the commonly used
high-carbon footprint approaches. In experiments with the ASVspoof 2019 LA
dataset, we achieve a 0.90\% equal error rate (EER) with less than 1k trainable
model parameters. To encourage further research in this direction and support
reproducible results, the Python code will be made publicly accessible
following acceptance. Github: this https URL

该研究提出了一种新的音频深度伪造检测框架，利用标准 CPU 资源进行无缝训练，并借助自我监督学习模型的嵌入来使用经典机器学习算法，与常用的高碳足迹方法相比，取得了有竞争力的结果。