由大脑启发的感知决策机器用于伪造语音检测
Chang Feng1, Xiaolong Wu2, Hamdulla Askar2
1Center for Speech and Language Technologies, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, 100084, China.
Scientific reports
|March 5, 2026
概括
这项研究引入了一种新的脑启发方法,用于检测由人工智能 (AI) 产生的假音频. 多线索检测范式增强了适应新型音频伪造类型的适应性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 人工智能产生的内容 (AIGC) 对伪造语音检测提出了不断变化的挑战.
- 现有的基于分类的方法难以将其推广为新的音频伪造技术,并且需要大量的训练数据.
研究的目的:
- 开发一个强大的和适应性的假语音检测系统.
- 为了克服当前处理各种和未见的音频伪造类型的方法的局限性.
主要方法:
- 引入一种受大脑启发的多线索检测范式.
- 一种具有独立检测器的感知决策机器,其检测器优化为最大检测精度 (MaxDP).
- 使用逻辑推理和可变长度OR操作进行增量学习的决策模块.
主要成果:
- 拟议的框架在检测音频伪造方面表现出有效性.
- 与传统方法相比,多线索方法显示了更好的概括能力.
- 该系统可实现无增量学习新的伪造线索,而无需完全重新培训.
结论:
- 多线索检测视角为伪造语音检测提供了一个有希望的方向.
- 拟议的框架增强了对新出现的音频威胁的可解释性和实际适应性.
- 这种方法代表了对抗复杂的人工智能生成的音频欺骗的重大进步.
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