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English(EN) Intellectual Property Protection of Hyperdimensional Computing Classifiers: Black-Box Ownership Verification via Model Watermarking

新研究探讨超高维计算分类器的水印技术

研究人员研究了用于超高维计算(HDC)分类器的模型水印技术,将基于后门的嵌入方法应用于三种不同的HDC模型。研究发现,像AdaptHD和OnlineHD这样的误差驱动分类器可以在准确性损失最小的情况下支持验证,而Vanilla HDC需要更多的暴露并且可能显著影响性能。水印在持续训练下表现出弹性,但容易被攻击者的水印提取和覆盖,尽管严重的覆盖也会导致准确性下降。在现场可编程门阵列(FPGA)上测试时,带水印的AdaptHD模型在存在大量计算错误的情况下也显示出验证的生存能力。 AI

影响 这项研究探索了保护高效AI模型知识产权的方法,可能影响AI在资源受限环境中的开发和部署。

排序理由 该集群包含一篇学术论文,详细介绍了关于超高维计算分类器模型水印的新研究发现。

在 arXiv cs.LG 阅读 →

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新研究探讨超高维计算分类器的水印技术

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该集群包含一篇学术论文,详细介绍了关于超高维计算分类器模型水印的新研究发现。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Hamed Poursiami, Farshad Soleiman, Ahmad Tahmasivand, Guojing Cong, Thomas Potok, Khaled N. Khasawneh, Kai Zeng, Maryam Parsa ·

    超高维计算分类器的知识产权保护:通过模型水印进行黑盒所有权验证

    arXiv:2610.34942v1 Announce Type: cross Abstract: Hyperdimensional computing (HDC) offers efficient learning and inference for resource-constrained systems, yet ownership verification of its models remains underexplored. We present the first systematic investigation of model wate…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Maryam Parsa ·

    超高维计算分类器的知识产权保护:通过模型水印进行黑盒所有权验证

    Hyperdimensional computing (HDC) offers efficient learning and inference for resource-constrained systems, yet ownership verification of its models remains underexplored. We present the first systematic investigation of model watermarking in HDC by adapting backdoor-based embeddi…