R-SNN: An Analysis and Design Methodology for Robustifying Spiking Neural Networks against Adversarial Attacks through Noise Filters for Dynamic Vision Sensors
This repository provides the source codes for applying Attacks on Dynamic Vision Sensors for Spiking Neural Networks, and for applying event-noise filters with our R-SNN methodology. If you used these results in your research, please refer to the paper
A. Marchisio, G. Pira, M. Martina, G. Masera and M. Shafique, "R-SNN: An Analysis and Design Methodology for Robustifying Spiking Neural Networks against Adversarial Attacks through Noise Filters for Dynamic Vision Sensors," 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Virtual Event, September 2021.
@INPROCEEDINGS{Marchisio2021DVSAttacks,
author={A. {Marchisio} and G. {Pira} and M. {Martina} and G. {Masera} and M. {Shafique}},
booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
title={R-SNN: An Analysis and Design Methodology for Robustifying Spiking Neural Networks against Adversarial Attacks through Noise Filters for Dynamic Vision Sensors},
year={2021},
volume={},
number={},
pages={}}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent