基于新型转移学习的声学特征工程为现场伪造音频检测
Ahmad Sami Al-Shamayleh1, Hafsa Riasat2, Ala Saleh Alluhaidan3
1Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Al-Ahliyya Amman University, Amman, 19328, Jordan.
Scientific reports
|March 7, 2025
概括
这项研究引入了一种新的转移学习方法,用于检测假音频,达到0.98准确度. 通过改进操纵音频检测,MfC-RF方法提高了数字通信的可靠性.
科学领域:
- 数字安全数字安全
- 音频法医学 音频法医学
- 机器学习 机器学习
背景情况:
- 音频取证对于法律和安全调查至关重要.
- 复杂的音频假冒攻击对假冒音频检测系统构成重大挑战.
- 操纵音频内容的日益增长的威胁影响了媒体,法律证据和网络安全.
研究的目的:
- 提出一种新的转移学习方法,用于增强假音频检测.
- 提高识别操纵音频内容的准确性和效率.
- 为数字通信的完整性和可靠性做出贡献.
主要方法:
- 使用了包含 12,668 个音频文件的 SceneFake 基准数据集.
- 提取的Mel-Frequency Cepstral系数 (MFCC) 和类预测概率特征.
- 开发了一种新的MfC-RF (MFCC-随机森林) 转移学习方法,用于特征提取和分类.
主要成果:
- 拟议的MfC-RF方法实现了0.98.8的高精度.
- 在伪造音频检测方面,MfC-RF方法的性能超过了现有的最先进的方法.
- 应用了超参数调整和交叉验证以确保强大的性能.
结论:
- 新的MfC-RF传输学习方法显著提高了假音频检测的准确性.
- 这项研究有助于更可靠的数字安全和法医分析.
- 这项研究证明了转移学习在打击复杂的音频操纵技术方面的有效性.
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