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English(EN) CBW: Towards Dataset Ownership Verification for Speaker Verification via Clustering-based Backdoor Watermarking

新的CBW方法验证说话人验证模型的数据集所有权

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为CBW（基于聚类的后门水印）的新方法，用于验证说话人验证模型中使用的训练数据集的所有权。现有方法在开放集场景（即发布后注册新身份）中存在困难。CBW通过将训练数据划分为簇并将唯一触发器分配给每个簇来解决此问题，确保了广泛的覆盖范围和保真度。该方法旨在抵抗水印移除并可转移到不同的模型架构。 AI

影响 这项研究引入了一种保护AI训练数据知识产权的方法，可能会影响数据集的许可和使用方式。

排序理由 该集群包含一篇详细介绍新技术的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的CBW方法验证说话人验证模型的数据集所有权

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该集群包含一篇详细介绍新技术的研究论文。[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) · Yiming Li, Kaiying Yan, Jiawen Diao, Shuo Shao, Tongqing Zhai, Shu-Tao Xia, Dacheng Tao ·

    CBW：通过基于聚类的后门水印实现说话人验证的数据集所有权验证

    arXiv:2503.05794v4 Announce Type: replace-cross Abstract: Speaker verification models are trained on large-scale public datasets whose licenses usually prohibit unauthorized commercial use, yet such infringement is difficult to detect or deter. Dataset ownership verification (DOV…

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