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    This study introduces novel methods to perform adversarial attacks on spiking neural networks (SNNs), addressing unique challenges in gradient computation for enhanced neuromorphic device security.

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    Area of Science:

    • Neuromorphic engineering
    • Artificial intelligence security
    • Computational neuroscience

    Background:

    • Spiking neural networks (SNNs) are increasingly used in neuromorphic devices, mimicking brain function.
    • The security of SNNs against adversarial attacks is a critical but under-researched area.
    • Existing artificial neural network (ANN) attack methods face challenges when applied to SNNs due to their spatiotemporal and binary nature.

    Purpose of the Study:

    • To develop effective adversarial attack methodologies for SNNs.
    • To address the challenges of gradient computation and incompatibility in SNNs.
    • To analyze the security vulnerabilities of SNNs and compare them with ANNs.

    Main Methods:

    • Proposed a gradient-to-spike (G2S) converter to handle continuous-to-ternary gradient conversion.
    • Introduced a restricted spike flipper (RSF) to manage all-zero gradients and enable spike manipulation.
    • Leveraged backpropagation through time (BPTT)-inspired algorithms for spatiotemporal gradient mapping.

    Main Results:

    • Successfully developed and validated an adversarial attack methodology for SNNs.
    • Demonstrated the effectiveness of G2S and RSF in overcoming gradient-related attack challenges.
    • Analyzed the impact of training loss functions and firing thresholds on attack success.

    Conclusions:

    • The proposed methods enable accurate adversarial attacks on SNNs, revealing security vulnerabilities.
    • This research highlights the distinct security landscape of SNNs compared to ANNs.
    • Findings encourage further investigation into SNN security and neuromorphic device protection.