新语音伪造攻击出现时，无需样本即可快速更新溯源模型。
Listen, Analyze, and Adapt to Learn New Attacks: An Exemplar-Free Class Incremental Learning Method for Audio Deepfake Source Tracing
- 固定特征提取器，用解析解一步更新分类器。
- 在1个训练周期内完成增量学习，准确率优于基线方法。
- 适合在线部署，保护数据隐私且节省内存。
随着语音深度伪造技术日益普遍且难以检测，溯源其来源至关重要。现有音频深度伪造溯源（ST）方法需在不遗忘旧攻击类型的前提下学习新攻击。主要挑战是灾难性遗忘问题。尽管部分持续学习方法可用于深度伪造检测，但多类别任务随类别增加带来额外困难。为此，本文提出一种分析型类增量学习方法AnaST。当出现新攻击时，保持特征提取器不变，分类器通过闭式解析解在单个训练周期内完成更新。该方法保障数据隐私，优化内存使用，适用于在线训练。实验表明，所提方法在多个数据集上均优于现有基线方法。
原文摘要 · Abstract (English)
As deepfake speech becomes common and hard to detect, it is vital to trace its source. Recent work on audio deepfake source tracing (ST) aims to find the origins of synthetic or manipulated speech. However, ST models must adapt to learn new deepfake attacks while retaining knowledge of the previous ones. A major challenge is catastrophic forgetting, where models lose the ability to recognize previously learned attacks. Some continual learning methods help with deepfake detection, but multi-class tasks such as ST introduce additional challenges as the number of classes grows. To address this, we propose an analytic class incremental learning method called AnaST. When new attacks appear, the feature extractor remains fixed, and the classifier is updated with a closed-form analytical solution in one epoch. This approach ensures data privacy, optimizes memory usage, and is suitable for online training. The experiments carried out in this work show that our method outperforms the baselines.
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