Robustness from Interaction: Event-Structure-Aware Attention for Event-Based Spiking Transformers

Robustness in event-based vision is often addressed before the network via event denoising, corruption-aware training schemes, or robust feature representations. We pose a complementary question: after noisy events have already been discretized and tokenized, can we improve robustness by controlling how event tokens interact with one another? Unlike generic sequence tokens, event tokens still encode sensing-imposed spatial and polarity structure inherited from the event generation process. We introduce Spiking Event-Consistent Attention (SECA), which maps this token-aligned structure into a bounded multiplicative modulation of learned query–key correlations, adding only two learnable scalars per block while keeping spiking dynamics intact. Without any corruption training (trained only on clean data), SECA maintains clean accuracy across three Spiking Transformer families and cuts the performance drop under the strongest polarity flip on DVS128-Gesture from $28.82$ to $9.38$ points, with consistent improvements under background activity and hot-pixel noise. Controlled violations of the assumed priors indicate that retaining event structure provides benefits beyond a generic bounded modulation, especially under polarity corruption. A test-time gate ablation further shows that much of the robustness remains even when the gate is turned off at inference, suggesting the structural constraint primarily shapes the learned solution rather than serving only as test-time suppression. Overall, these findings position interaction-level robustness as a complementary design principle for event-based Spiking Transformers.