The emergence of new spoofing attacks poses an increasing challenge to audio security. Current detection methods often falter when faced with unseen spoofing attacks. Traditional strategies, such as retraining with new data, are not always feasible due to extensive storage. This paper introduces a novel continual learning method Continual Audio Defense Enhancer (CADE). First, by utilizing a fixed memory size to store randomly selected samples from previous datasets, our approach conserves resources and adheres to privacy constraints. Additionally, we also apply two distillation losses in CADE. By distillation in classifiers, CADE ensures that the student model closely resembles that of the teacher model. This resemblance helps the model retain old information while facing unseen data. We further refine our model's performance with a novel embedding similarity loss that extends across multiple depth layers, facilitating superior positive sample alignment. Experiments conducted on the ASVspoof2019 dataset show that our proposed method outperforms the baseline methods.

翻译：新型伪造攻击的出现对音频安全构成了日益严峻的挑战。当前的检测方法在面对未见过的伪造攻击时常常失效。由于需要大量存储空间，传统的策略（如使用新数据重新训练模型）往往并不可行。本文提出了一种新颖的持续学习方法——持续音频防御增强器（CADE）。首先，通过利用固定大小的记忆库来存储从先前数据集中随机选取的样本，我们的方法能够节省资源并遵守隐私约束。此外，我们还在CADE中应用了两种蒸馏损失。通过在分类器中进行蒸馏，CADE确保学生模型能够紧密地模仿教师模型。这种相似性有助于模型在面对未见数据时保留旧知识。我们进一步通过一种新颖的嵌入相似性损失来优化模型性能，该损失扩展到多个深度层，促进了更优的正样本对齐。在ASVspoof2019数据集上进行的实验表明，我们提出的方法优于基线方法。