mlsanityData to 14 Oct 2026

Adversarial Attacks on Spiking Convolutional Neural Networks for Event-based Vision

arXiv 2110.02929 ↗First posted 6 Oct 2021cs.CVcited by 14

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Abstract (arXiv)

Event-based dynamic vision sensors provide very sparse output in the form of spikes, which makes them suitable for low-power applications. Convolutional spiking neural networks model such event-based data and develop their full energy-saving potential when deployed on asynchronous neuromorphic hardware. Event-based vision being a nascent field, the sensitivity of spiking neural networks to potentially malicious adversarial attacks has received little attention so far. We show how white-box adversarial attack algorithms can be adapted to the discrete and sparse nature of event-based visual data, and demonstrate smaller perturbation magnitudes at higher success rates than the current state-of-the-art algorithms. For the first time, we also verify the effectiveness of these perturbations directly on neuromorphic hardware. Finally, we discuss the properties of the resulting perturbations, the effect of adversarial training as a defense strategy, and future directions.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 21 of its 38 references that are in the map. First posted on arXiv 6 Oct 2021.

Builds on

21 of 38 references.

References · 21

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  12. 2015Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccadescited by 314
9 more
  1. 2018SparseFool: a few pixels make a big differencecited by 94
  2. 2016Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computingcited by 70
  3. 2017A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)cited by 52
  4. 2020Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activationscited by 48
  5. 2019Optimizing the energy consumption of spiking neural networks for neuromorphic applicationscited by 37
  6. 2020Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradientcited by 31
  7. 2021DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networkscited by 21
  8. 2018Adversarial Training for Probabilistic Spiking Neural Networkscited by 11
  9. 2020Adversarial Attacks on Binary Image Recognition Systemscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 5; 2026: 4.

14 papers.

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  1. 2024State Space Models for Event Camerascited by 75
  2. 2025Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate Gradientscited by 9
  3. 2023Adversarially Robust Spiking Neural Networks Through Conversioncited by 7
  4. 2025Input-Triggered Hardware Trojan Attack on Spiking Neural Networkscited by 5
  5. 2026Time Is All It Takes: Spike-Retiming Attacks on Event-Driven Spiking Neural Networkscited by 5
  6. 2024Time-Distributed Backdoor Attacks on Federated Spiking Learningcited by 4
  7. 2024Flashy Backdoor: Real-world Environment Backdoor Attack on SNNs with DVS Camerascited by 3
  8. 2025Input-Specific and Universal Adversarial Attack Generation for Spiking Neural Networks in the Spiking Domaincited by 1
  9. 2024Advancing Neuromorphic Computing: Mixed-Signal Design Techniques Leveraging Brain Code Units and Fundamental Code Unitscited by 0
  10. 2025On the Adversarial Robustness of Spiking Neural Networks Trained by Local Learningcited by 0
  11. 2025Adversarially Robust Spiking Neural Networks with Sparse Connectivitycited by 0
  12. 2026Generating Adversarial Events: A Motion-Aware Point Cloud Frameworkcited by 0
2 more
  1. 2026Driving up Inference Energy on SNNs: Per-Sample and Universal Sponge Attackscited by 0
  2. 2026Stealth Is a Relation, Not a Property: How Event Representations Create Blind Spots for Timing Attacks in Event-Based Perceptioncited by 0