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arXiv Machine Learning By Hamed Poursiami, Farshad Soleiman, Ahmad Tahmasivand, Guojing Cong, Thomas Potok, Khaled N. Khasawneh, Kai Zeng, Maryam Parsa

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

Read the original on arXiv Machine Learning →

The paper investigates model watermarking for hyperdimensional computing (HDC) classifiers, adapting backdoor-based embedding to three prototype‑learning mechanisms. Across four benchmarks, error‑driven models (AdaptHD and OnlineHD) support ownership verification with minimal accuracy loss, while single‑pass Vanilla HDC requires higher trigger exposure and incurs larger accuracy penalties. The study also shows that watermarks can be overwritten, survive certain stress tests on FPGA, but may be lost during extraction or severe stress, highlighting both feasibility and limitations of HDC watermarking.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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