使用"混合粘贴"命令检测伪造的音频文件:基于韩国语音特征的深度学习方法
1Department of Digital Media, Soongsil University, Seoul 07027, Republic of Korea.
Sensors (Basel, Switzerland)
|March 28, 2024
概括
这项研究引入了一种用于验证语音录音的新型深度学习模型,在检测伪造音频文件方面达到97.5%的准确性. 该方法有效地识别操纵的音频证据,这对于法律诉讼至关重要.
科学领域:
- 数字法医学数字法医学
- 人工智能的人工智能
- 音频工程 音频工程
背景情况:
- 语音录音越来越多地被用作法律案件中的数字证据.
- 音频文件伪造的指控正在上升,需要强大的身份验证方法.
- 现有的法医技术很难检测到像"混合粘贴"这样的复杂音频操纵.
研究的目的:
- 开发和验证用于识别伪造语音录音的深度学习方法.
- 为了特别解决使用"混合粘贴"技术修改的音频文件的检测.
- 在法律背景下提高数字音频证据的可靠性.
主要方法:
- 开发了一种混合深度学习模型,将卷积神经网络 (CNN) 和长短期记忆 (LSTM) 结合起来.
- 从谱图和韩国辅音类型的序列中提取特征以进行分析.
- 该模型是通过来自iPhone的真实和"混合粘贴"伪造音频录音的数据集进行训练的.
主要成果:
- 混合深度学习模型在检测伪造的音频文件方面实现了97.5%的高准确率.
- 该模型的有效性在不同智能手机型号和音频编辑软件中是一致的.
- 验证测试证实了该模型能够识别操纵的音频文件的能力.
结论:
- 拟议的深度学习框架为数字取证中的音频文件身份验证提供了一个强大的新工具.
- 这项研究通过引入一种能够检测"混合粘贴"音频伪造的新型混合模型来推进音频法医学.
- 这些发现支持人工智能的更广泛应用,以确保数字证据的完整性.
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