在资源有限的音频系统中,具有动态可学习的稀疏注意力和三模融合的伪造检测
Xinwei Wang1, Zhicheng Tan2, Guo Li2
1Department of Forensic Science, Fu Jian Police College, Fuzhou, Fujian, China.
PloS one
|December 31, 2025
概括
这项研究引入了一个新的动态可学习的稀疏注意力 (DLSA) 框架,用于在自动扬声器验证 (ASV) 系统中有效检测音频伪造. DLSA方法显著降低了计算成本,同时提高了资源有限的设备上的检测准确性.
科学领域:
- 语音处理 语音处理
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 自动扬声器验证 (ASV) 系统容易受到音频伪造攻击.
- 现有的伪造检测方法,如多头注意力 (MHA),在计算上昂贵且内存密集,限制了它们在资源有限的环境中使用.
研究的目的:
- 为资源有限的ASV系统开发一个高效和强大的伪造检测框架.
- 为了减少伪造检测方法的计算复杂性和内存足迹.
主要方法:
- 提出了一个新的动态可学习的稀疏注意力 (DLSA) 框架,集成Mel-Frequency Cepstral系数 (MFCC),常数-Q转换 (CQT) 和原始波形.
- 采用ResNet骨干用于原始波形特征提取和混合损失函数 (交叉和中心损失) 以优化特征表示.
- 实施了一种可学习的注意力机制,用于频谱和时间特征的动态交叉模式融合.
主要成果:
- 与基于MHA的方法相比,计算成本降低了80%.
- 在ASVspoof 2019的LA数据集上实现了0.68%的等错率 (EER) 和0.0173的最小并联检测成本函数 (t-DCF).
- 与现有方法相比,在EER减少方面表现出33.6%的改善.
结论:
- DLSA框架提供了一个高效和有效的解决方案,用于在资源有限的ASV系统中检测伪造.
- 拟议的方法提高了对复杂的音频伪造技术的稳定性.
- 这项工作为在边缘设备上部署高级安全功能铺平了道路.
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