DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networks

Alberto Marchisio, Giacomo Pira, Maurizio Martina, Guido Masera, Muhammad Shafique

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Spiking Neural Networks (SNNs), despite being energy-efficient when implemented on neuromorphic hardware and coupled with event-based Dynamic Vision Sensors (DVS), are vulnerable to security threats, such as adversarial attacks, i.e., small perturbations added to the input for inducing a misclassification. Toward this, we propose DVS-Attacks, a set of stealthy yet efficient adversarial attack methodologies targeted to perturb the event sequences that compose the input of the SNNs. First, we show that noise filters for DVS can be used as defense mechanisms against adversarial attacks. Afterwards, we implement several attacks and test them in the presence of two types of noise filters for DVS cameras. The experimental results show that the filters can only partially defend the SNNs against our proposed DVS-Attacks. Using the best settings for the noise filters, our proposed Mask Filter-Aware Dash Attack reduces the accuracy by more than 20% on the DVS-Gesture dataset and by more than 65% on the MNIST dataset, compared to the original clean frames. The source code of all the proposed DVS-Attacks and noise filters is released at https://github.com/albertomarchisio/DVS-Attacks.

Original languageEnglish (US)
Title of host publicationIJCNN 2021 - International Joint Conference on Neural Networks, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780738133669
StatePublished - Jul 18 2021
Event2021 International Joint Conference on Neural Networks, IJCNN 2021 - Virtual, Shenzhen, China
Duration: Jul 18 2021Jul 22 2021

Publication series

NameProceedings of the International Joint Conference on Neural Networks


Conference2021 International Joint Conference on Neural Networks, IJCNN 2021
CityVirtual, Shenzhen


  • Adversarial Attacks
  • DVS
  • Deep Learning
  • Defense
  • Dynamic Vision Sensors
  • Event-Based
  • Filter
  • Neuromorphic
  • Noise
  • Perturbation
  • Robustness
  • SNNs
  • Security
  • Spiking Neural Networks

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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