提出监测语音伪造数据漂移的方法，提升云上检测器的持续有效性
Towards Data Drift Monitoring for Speech Deepfake Detection in the context of MLOps
- 用新数据与参考数据分布距离监控语音伪造漂移
- 通过新伪造数据微调模型，降低漂移并减少误检率
- 适合关注模型持续性与安全的MLOps实践者
在云端应用中，未更新的静态语音伪造检测器会因新型攻击而失效。从机器学习运维（MLOps）视角出发，本文探讨能否监测未见的新语音伪造数据是否偏离已有参考数据集。若检测到漂移，是否可通过类似漂移数据微调检测器以减小偏差并提升性能。在小型玩具数据集和大规模MLAAD数据集上，实验表明：利用新数据与参考数据分布之间的距离可有效监测由新型文本转语音（TTS）攻击引发的数据漂移；进一步证明，使用新生成的TTS伪造数据微调检测器，可显著降低漂移程度与检测错误率。
原文摘要 · Abstract (English)
When being delivered in applications or services on the cloud, static speech deepfake detectors that are not updated will become vulnerable to newly created speech deepfake attacks. From the perspective of machine learning operations (MLOps), this paper tries to answer whether we can monitor new and unseen speech deepfake data that drifts away from a seen reference data set. We further ask, if drift is detected, whether we can fine-tune the detector using similarly drifted data, reduce the drift, and improve the detection performance. On a toy dataset and the large-scale MLAAD dataset, we show that the drift caused by new text-to-speech (TTS) attacks can be monitored using distances between the distributions of the new data and reference data. Furthermore, we demonstrate that fine-tuning the detector using data generated by the new TTS deepfakes can reduce the drift and the detection error rates.
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