# Adversarial Training for Probabilistic Spiking Neural Networks

arXiv: https://arxiv.org/abs/1802.08567 · HTML: https://mlsanity.com/paper/1802.08567/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2018.

## Built on by (most cited first, 11 of 11)

- [DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networks](https://mlsanity.com/paper/2107.00415/) (2021)
- [Exploring Inconsistent Knowledge Distillation for Object Detection with Data Augmentation](https://mlsanity.com/paper/2209.09841/) (2022)
- [Adversarial Attacks on Spiking Convolutional Neural Networks for Event-based Vision](https://mlsanity.com/paper/2110.02929/) (2021)
- [R-SNN: An Analysis and Design Methodology for Robustifying Spiking Neural Networks against Adversarial Attacks through Noise Filters for Dynamic Vision Sensors](https://mlsanity.com/paper/2109.00533/) (2021)
- [Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems](https://mlsanity.com/paper/2204.09514/) (2022)
- [Spiking NeRF: Representing the Real-World Geometry by a Discontinuous Representation](https://mlsanity.com/paper/2311.09077/) (2023)
- [Input-Triggered Hardware Trojan Attack on Spiking Neural Networks](https://mlsanity.com/paper/2503.21793/) (2025)
- [Analysis of Power-Oriented Fault Injection Attacks on Spiking Neural Networks](https://mlsanity.com/paper/2204.04768/) (2022)
- [Input-Specific and Universal Adversarial Attack Generation for Spiking Neural Networks in the Spiking Domain](https://mlsanity.com/paper/2505.06299/) (2025)
- [Influence of Parallelism in Vector-Multiplication Units on Correlation Power Analysis](https://mlsanity.com/paper/2601.05828/) (2026)
- [Driving up Inference Energy on SNNs: Per-Sample and Universal Sponge Attacks](https://mlsanity.com/paper/2607.27990/) (2026)

## Abstract (arXiv)

Classifiers trained using conventional empirical risk minimization or maximum likelihood methods are known to suffer dramatic performance degradations when tested over examples adversarially selected based on knowledge of the classifier's decision rule. Due to the prominence of Artificial Neural Networks (ANNs) as classifiers, their sensitivity to adversarial examples, as well as robust training schemes, have been recently the subject of intense investigation. In this paper, for the first time, the sensitivity of spiking neural networks (SNNs), or third-generation neural networks, to adversarial examples is studied. The study considers rate and time encoding, as well as rate and first-to-spike decoding. Furthermore, a robust training mechanism is proposed that is demonstrated to enhance the performance of SNNs under white-box attacks.