Spyridon Raptis

Driving up Inference Energy on SNNs: Per-Sample and Universal Sponge Attacks

This work develops a per-sample sponge attack that crafts a custom adversarial spike train for each input via gradient-based optimization and introduces the first universal sponge attack for native event-based SNN inputs: a fixed binary perturbation computed offline and applied via XOR to all subsequent inputs.