SpikeTimer: Exploring Active Copyright Protection in Spiking Neural Networks via Temporal Backdoor Regularization

Summary

SpikeTimer is a copyright protection framework for Spiking Neural Networks (SNNs) that uses temporal backdoor learning. It partitions neuromorphic data into timeslices and embeds authorized tokens only within authorized slices, so the model responds correctly to authorized inputs and erroneously to unauthorized ones. Evaluations on multiple neuromorphic datasets report about 10% accuracy on unauthorized data and about 1.5% degradation on authorized inputs, with resistance to fine-tuning and pruning.

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