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

Hamed Poursiami, Farshad Soleiman, Ahmad Tahmasivand, Guojing Cong, Thomas Potok +3

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 embedding to three classifiers with different prototype-learning mechanisms. Across four benchmarks, with conventional neural networks as references, we examine whether watermarks can be learned without compromising task accuracy and whether they survive subsequent model modification and replication. The error-driven classifiers, AdaptHD and OnlineHD, support verification at a nominal trigger exposure of 1% with at most 0.58 percentage points of mean accuracy loss, whereas single-pass Vanilla HDC requires greater exposure and can incur substantial accuracy costs. The error-driven models retain verification under native continued training, but extraction produces surrogates that reproduce much of the task behavior without verifiable watermarks. Watermark overwriting readily embeds attacker watermarks into HDC prototypes and erodes the owner's watermark more strongly than in the neural network references, although…