HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds

20 August 2023

Peng Li

Papers citing "HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds"

44 / 44 papers shown

Title
Flashy Backdoor: Real-world Environment Backdoor Attack on SNNs with DVS Cameras Roberto Riaño Gorka Abad S. Picek A. Urbieta AAML 75 0 0 05 Nov 2024
Robust Stable Spiking Neural Networks Jianhao Ding Zhiyu Pan Yujia Liu Zhaofei Yu Tiejun Huang AAML 76 7 0 31 May 2024
Enhancing Adversarial Robustness in SNNs with Sparse Gradients Yujia Liu Tong Bu Jianhao Ding Zecheng Hao Tiejun Huang Zhaofei Yu AAML 76 5 0 30 May 2024
Adversarially Robust Spiking Neural Networks Through Conversion Ozan Özdenizci Robert Legenstein AAML 56 10 0 15 Nov 2023
Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples Nuo Xu Kaleel Mahmood Haowen Fang Ethan Rathbun Caiwen Ding Wujie Wen AAML 57 13 0 07 Sep 2022
Toward Robust Spiking Neural Network Against Adversarial Perturbation Ling Liang Kaidi Xu Xing Hu Lei Deng Yuan Xie AAML 50 16 0 12 Apr 2022
HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noise Souvik Kundu Massoud Pedram Peter A. Beerel AAML 68 75 0 06 Oct 2021
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them Florian Tramèr AAML 71 68 0 24 Jul 2021
Securing Deep Spiking Neural Networks against Adversarial Attacks through Inherent Structural Parameters Rida El-Allami Alberto Marchisio Mohamed Bennai Ihsen Alouani AAML 56 39 0 09 Dec 2020
Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey S. Silva Peyman Najafirad AAML OOD 47 134 0 01 Jul 2020
Towards Understanding the Effect of Leak in Spiking Neural Networks Sayeed Shafayet Chowdhury Chankyu Lee Kaushik Roy 44 57 0 15 Jun 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 136 89 0 23 Mar 2020
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks Francesco Croce Matthias Hein AAML 213 1,842 0 03 Mar 2020
Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks Wenrui Zhang Peng Li 71 220 0 24 Feb 2020
Rapid online learning and robust recall in a neuromorphic olfactory circuit N. Imam T. A. Cleland 52 141 0 17 Jun 2019
A Comprehensive Analysis on Adversarial Robustness of Spiking Neural Networks Saima Sharmin Priyadarshini Panda Syed Shakib Sarwar Chankyu Lee Wachirawit Ponghiran Kaushik Roy AAML 44 67 0 07 May 2019
Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran Aleksander Madry SILM 89 1,839 0 06 May 2019
Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks Aamir Mustafa Salman Khan Munawar Hayat Roland Göcke Jianbing Shen Ling Shao AAML 56 152 0 01 Apr 2019
Enabling Spike-based Backpropagation for Training Deep Neural Network Architectures Chankyu Lee Syed Shakib Sarwar Priyadarshini Panda G. Srinivasan Kaushik Roy 76 396 0 15 Mar 2019
On Evaluating Adversarial Robustness Nicholas Carlini Anish Athalye Nicolas Papernot Wieland Brendel Jonas Rauber Dimitris Tsipras Ian Goodfellow Aleksander Madry Alexey Kurakin ELM AAML 81 901 0 18 Feb 2019
Adversarial Examples Are a Natural Consequence of Test Error in Noise Nic Ford Justin Gilmer Nicholas Carlini E. D. Cubuk AAML 83 319 0 29 Jan 2019
Surrogate Gradient Learning in Spiking Neural Networks Emre Neftci Hesham Mostafa Friedemann Zenke 87 1,236 0 28 Jan 2019
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift Stephan Rabanser Stephan Günnemann Zachary Chase Lipton 54 367 0 29 Oct 2018
Adversarial Attacks and Defences: A Survey Anirban Chakraborty Manaar Alam Vishal Dey Anupam Chattopadhyay Debdeep Mukhopadhyay AAML OOD 69 679 0 28 Sep 2018
Long short-term memory and learning-to-learn in networks of spiking neurons G. Bellec Darjan Salaj Anand Subramoney Robert Legenstein Wolfgang Maass 137 487 0 26 Mar 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 216 3,185 0 01 Feb 2018
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey Naveed Akhtar Ajmal Mian AAML 93 1,868 0 02 Jan 2018
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning Battista Biggio Fabio Roli AAML 122 1,409 0 08 Dec 2017
Mitigating Adversarial Effects Through Randomization Cihang Xie Jianyu Wang Zhishuai Zhang Zhou Ren Alan Yuille AAML 113 1,058 0 06 Nov 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 280 8,878 0 25 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 301 12,063 0 19 Jun 2017
Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks Yujie Wu Lei Deng Guoqi Li Jun Zhu Luping Shi 62 1,021 0 08 Jun 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 118 1,857 0 20 May 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 177 2,725 0 19 May 2017
On Detecting Adversarial Perturbations J. H. Metzen Tim Genewein Volker Fischer Bastian Bischoff AAML 61 950 0 14 Feb 2017
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra FAtt 294 19,981 0 07 Oct 2016
Robustness of classifiers: from adversarial to random noise Alhussein Fawzi Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 87 374 0 31 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 534 5,897 0 08 Jul 2016
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples Nicolas Papernot Patrick McDaniel Ian Goodfellow SILM AAML 112 1,740 0 24 May 2016
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 148 4,895 0 14 Nov 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 271 19,045 0 20 Dec 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 1.6K 100,330 0 04 Sep 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 268 14,912 1 21 Dec 2013
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 589 15,876 0 12 Nov 2013