Temporal Poisoning: Clean-Label Backdoors via Event Redistribution in SNNs
Backdoor attacks on Spiking Neural Networks (SNNs) have primarily assumed dirty-label poisoning, in which triggered training samples are relabeled to an attacker-selected class. We study clean-label temporal poisoning, where a fixed timestamp transformation is applied only to the target-class training streams, leaving their labels unchanged. The transformation preserves the per-pixel, per-polarity event count exactly, making clean and triggered samples identical after temporal aggregation while altering the sequence processed by the SNN. Across three neuromorphic datasets and both convolutional and transformer-based victims, the attack reaches an ASR of 1.00 in the strongest configuration...
| Authors | Roberto Riaño, Gorka Abad, Stjepan Picek et al. |
|---|---|
| Primary category | cs.CR |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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