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对于逻辑访问音频伪造的DeepLASD对策
Hamed Al-Tairi1, Ali Javed2, Tasawer Khan3
1School of Information Technology, Whitecliffe College, Auckland, New Zealand.
Scientific reports
|July 2, 2025
概括
本研究介绍了DeepLASD,这是一个端到端的深度学习系统,用于检测身份验证系统中的语音伪造攻击. 它有效地识别了使用原始音频波形的复杂语音转换和文本对语音威胁.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 基于语音的认证系统面临着使用先进的语音转换 (VC) 和文本到语音 (TTS) 技术的逻辑访问 (LA) 伪造的威胁.
- 现有的方法通常依赖于手工制作的功能,这些功能可能无法捕捉复杂的伪造攻击的细微差别.
研究的目的:
- 提出和评估DeepLASD,这是一个端到端的深度学习框架,用于检测语音身份验证中的LA伪造.
- 直接处理原始音频波形,消除了手动功能工程的需要.
主要方法:
- DeepLASD模型使用SincConv进行光谱处理和残余卷积块,并关注特征提取.
- GeLU激活被纳入剩余块中,以更好地区分真实和伪造的语音.
- 一个封闭的循环单元模拟时间动态,以增强伪造检测.
主要成果:
- 拟议的方法在ASVspoof 2019和2021数据集上实现了低的等错率 (EER) 和0.1208的最小并列检测成本函数 (t-DCF).
- 对VC和TTS伪造技术表现出强大的概括能力.
- 展示了显著的适应能力,以下一代合成语音,尽管正在进行的挑战.
结论:
- 在语音认证系统中,DeepLASD提供了一种有效和有效的解决方案,用于检测语音认证系统中的逻辑访问伪造.
- 端到端的框架增强了安全和检测能力,提高了基于语音的生物识别的稳定性.
- 这项研究强调了深度学习在实时反伪造应用中的潜力.
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