mlsanityData to 7 Oct 2026

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

arXiv 2107.00415 ↗First posted 1 Jul 2021cs.CV cs.LGcited by 21

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Abstract (arXiv)

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.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 24 of its 43 references that are in the map. First posted on arXiv 1 Jul 2021.

Builds on

24 of 43 references.

References · 24

  1. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  2. 2013Intriguing properties of neural networkscited by 5,114
  3. 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,510
  4. 2015Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccadescited by 308
  5. 2018SLAYER: Spike Layer Error Reassignment in Timecited by 257
  6. 2017A Survey of Neuromorphic Computing and Neural Networks in Hardwarecited by 82
  7. 2018Sparse Adversarial Perturbations for Videoscited by 56
  8. 2020Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activationscited by 47
  9. 2021Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Aheadcited by 40
  10. 2019A Comprehensive Analysis on Adversarial Robustness of Spiking Neural Networkscited by 35
  11. 2020Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Aheadcited by 34
  12. 2020FSpiNN: An Optimization Framework for Memory- and Energy-Efficient Spiking Neural Networkscited by 32
12 more
  1. 2020Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradientcited by 31
  2. 2021SpikeDyn: A Framework for Energy-Efficient Spiking Neural Networks with Continual and Unsupervised Learning Capabilities in Dynamic Environmentscited by 29
  3. 2020Securing Deep Spiking Neural Networks against Adversarial Attacks through Inherent Structural Parameterscited by 27
  4. 2020An Efficient Spiking Neural Network for Recognizing Gestures with a DVS Camera on the Loihi Neuromorphic Processorcited by 25
  5. 2020NeuroAttack: Undermining Spiking Neural Networks Security through Externally Triggered Bit-Flipscited by 21
  6. 2021SparkXD: A Framework for Resilient and Energy-Efficient Spiking Neural Network Inference using Approximate DRAMcited by 21
  7. 2018Adversarial Framing for Image and Video Classificationcited by 15
  8. 2018Adversarial Training for Probabilistic Spiking Neural Networkscited by 11
  9. 2019Deep SCNN-based Real-time Object Detection for Self-driving Vehicles Using LiDAR Temporal Datacited by 8
  10. 2017Neural Networks for Safety-Critical Applications - Challenges, Experiments and Perspectivescited by 7
  11. 2018Gridbot: An autonomous robot controlled by a Spiking Neural Network mimicking the brain's navigational systemcited by 7
  12. 2018FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learningcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 2; 2023: 3; 2024: 4; 2025: 6; 2026: 3.

21 papers.

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  1. 2023Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarkscited by 90
  2. 2021Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Frameworkcited by 17
  3. 2021Adversarial Attacks on Spiking Convolutional Neural Networks for Event-based Visioncited by 14
  4. 2022Toward Robust Spiking Neural Network Against Adversarial Perturbationcited by 12
  5. 2021Adversarial Attack for Asynchronous Event-based Datacited by 9
  6. 2024Enhancing Adversarial Robustness in SNNs with Sparse Gradientscited by 9
  7. 2025Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate Gradientscited by 9
  8. 2023Adversarially Robust Spiking Neural Networks Through Conversioncited by 7
  9. 2024Embodied Neuromorphic Artificial Intelligence for Robotics: Perspectives, Challenges, and Research Development Stackcited by 7
  10. 2022Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systemscited by 6
  11. 2025Adversarial Attacks on Event-Based Pedestrian Detectors: A Physical Approachcited by 5
  12. 2026Time Is All It Takes: Spike-Retiming Attacks on Event-Driven Spiking Neural Networkscited by 5
9 more
  1. 2025Input-Triggered Hardware Trojan Attack on Spiking Neural Networkscited by 4
  2. 2024Flashy Backdoor: Real-world Environment Backdoor Attack on SNNs with DVS Camerascited by 3
  3. 2024NEUROSEC: FPGA-Based Neuromorphic Audio Securitycited by 2
  4. 2025Input-Specific and Universal Adversarial Attack Generation for Spiking Neural Networks in the Spiking Domaincited by 1
  5. 2023Security-Aware Approximate Spiking Neural Networkscited by 0
  6. 2025On the Adversarial Robustness of Spiking Neural Networks Trained by Local Learningcited by 0
  7. 2025Adversarially Robust Spiking Neural Networks with Sparse Connectivitycited by 0
  8. 2026Driving up Inference Energy on SNNs: Per-Sample and Universal Sponge Attackscited by 0
  9. 2026Stealth Is a Relation, Not a Property: How Event Representations Create Blind Spots for Timing Attacks in Event-Based Perceptioncited by 0