Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples
arXiv:2209.03358v5 Announce Type: replace-cross Abstract: Spiking neural networks (SNNs) have attracted much attention for their high energy efficiency and recent advances in classification performance. However, unlike traditional deep learning approaches, the study of SNN robustness to adversarial examples remains relatively underdeveloped. In this work, we advance the adversarial attack side of SNNs through three contributions. First, we show that successful white-box adversarial attacks on SNNs are highly dependent on the underlying surrogate gradient estimator, even for adversarially train
The increasing adoption and advancement of Spiking Neural Networks (SNNs) for energy-efficient AI computation makes understanding their security vulnerabilities critically timely.
As SNNs are deployed in practical applications, particularly those requiring real-time processing and energy efficiency, their susceptibility to adversarial attacks could pose significant security and reliability risks.
This research provides deeper insight into the specific methods and dependencies of adversarial attacks against SNNs, shifting the focus towards developing more robust and secure SNN architectures.
- · AI security researchers
- · Developers of robust SNN architectures
- · Sectors adopting energy-efficient AI
- · Vulnerable SNN deployments
- · Early SNN adopters without robust security measures
Increased research and development into adversarial training and defense mechanisms specifically for SNNs will accelerate.
The development pathway for SNNs might diverge to prioritize security over pure energetic efficiency in certain sensitive applications.
A potential arms race between SNN attack and defense methodologies could emerge, mirroring the dynamics seen in traditional deep learning.
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Read at arXiv cs.LG