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Threaten Spiking Neural Networks through Combining Rate and Temporal Information

ICLR 2024 · Adversarial robustness and security › Attacks on text and graphs · approach: Graph neural network robustness

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Studies adversarial robustness of spiking neural networks by combining rate and temporal information from the two mainstream SNN learning algorithms; only the abstract was available.

Problem

Spiking neural networks are increasingly deployed, but their vulnerability to adversarial attacks is a growing concern.

Approach

Draws on the two mainstream SNN learning algorithms (rate-based and temporal-based) and combines rate and temporal information to address SNN adversarial threats; specific mechanism not stated in the available abstract.

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