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English(EN) Time-Frequency Consistency Learning for Robust Speech Deepfake Detection

新框架增强语音深度伪造检测对音频失真的鲁棒性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个名为时频一致性学习（TFCL）的新框架，以提高语音深度伪造检测系统的鲁棒性。当前的检测模型在面对声学前端处理引入的真实世界音频失真（如噪声抑制和语音活动检测）时常常表现不佳。TFCL通过学习对时间偏移和频域失真都不变的表示来解决这个问题，从而提高在复杂声学环境中的检测准确性。 AI

影响 提高了用于检测被篡改音频内容的AI系统的可靠性。

排序理由 详细介绍AI问题新技术方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新框架增强语音深度伪造检测对音频失真的鲁棒性

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详细介绍AI问题新技术方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jun Xue, Zhuolin Yi, Yanzhen Ren, Yihuan Huang, Jiayu Xiong, Yi Chai, Guanxiang Feng, Jiajun Liu, Tong Zhang ·

    面向鲁棒语音深度伪造检测的时频一致性学习

    arXiv:2607.17761v1 Announce Type: cross Abstract: Recently, speech deepfake detection (SDD) has achieved significant progress. However, its robustness evaluation remains largely confined to controlled additive noise scenarios, lacking systematic investigation of the complex disto…

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