Doohyun Kim

Timestep-Compressed Attack on Spiking Neural Networks Through Timestep-Level Backpropagation

State-of-the-art (SOTA) gradient-based adversarial attacks on spiking neural networks (SNNs), which largely rely on extending FGSM and PGD frameworks, face a critical limitation: substantial attack latency from multi-timestep processing, rendering them infeasible for practical real-time applications