Attacking the Spike: On the Transferability and Security of Spiking
Neural Networks to Adversarial Examples

Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples

7 September 2022

Ethan Rathbun

Caiwen Ding

Papers citing "Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples"

5 / 5 papers shown

Title
HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds Hejia Geng Peng Li AAML 27 3 0 20 Aug 2023
Deep Residual Learning in Spiking Neural Networks Wei Fang Zhaofei Yu Yanqing Chen Tiejun Huang T. Masquelier Yonghong Tian 121 470 0 08 Feb 2021
Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation Nitin Rathi G. Srinivasan Priyadarshini Panda Kaushik Roy 116 292 0 04 May 2020
Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations Saima Sharmin Nitin Rathi Priyadarshini Panda Kaushik Roy AAML 106 85 0 23 Mar 2020
Long short-term memory and learning-to-learn in networks of spiking neurons G. Bellec Darjan Salaj Anand Subramoney R. Legenstein Wolfgang Maass 111 477 0 26 Mar 2018