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20212026
most citedSneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Data

13 citations · 18 across the 13 of their papers we have counts for

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cs.CR2026

Beyond Scalar Probes: Exploiting Vector-Valued Outputs in ReLU Networks For Signature Extraction

Gorka Abad, Claude Carlet, Ermes Franch +2

We revisit cryptanalytic extraction of ReLU networks from a geometric and algebraic perspective. Rather than restricting attention to a single output component, we study the full v…

cs.CR2026

When the World Lies: Backdoor Attacks on Latent World Models for Downstream Control

Roberto Riaño, Gorka Abad, Stjepan Picek +1

Pretrained world models, learned simulators that encode an observation into a latent state and predict how it evolves under actions, are beginning to be reused as off-the-shelf dyn…

cs.CR2026

MeMark: Membrane-Space Watermarking for Spiking Neural Networks

Roberto Riaño, Gorka Abad, Stjepan Picek +1

Spiking Neural Networks (SNNs) are increasingly distributed as pretrained checkpoints and reused as backbones for new tasks. However, current SNN watermarks are mainly verified aga…

cs.CR2026

Temporal Poisoning: Clean-Label Backdoors via Event Redistribution in SNNs

Roberto Riaño, Gorka Abad, Stjepan Picek +1

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…

cs.CV2026

Removing the Trigger, Not the Backdoor: Alternative Triggers and Latent Backdoors

Gorka Abad, Ermes Franch, Stefanos Koffas +1

Current backdoor defenses assume that neutralizing a known trigger removes the backdoor. We show this trigger-centric view is incomplete: \emph{alternative triggers}, patterns perc…