:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Lun, Li, Feng, Kunyu, Ni, Qinglong, Liang, Ling, Wang, Yuan, Li, Ying, Yu, Dunshan, Cui, Xiaoxin
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition
Online Access:	https://arxiv.org/abs/2503.03272
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Directly Training Temporal Spiking Neural Network with Sparse Surrogate Gradient
by: Li, Yang, et al.
Published: (2024)

Fiber Transistors as a Hardware Surrogate Gradient for Backpropagation in Spiking Neural Networks
by: Weichu Chen, et al.
Published: (2025)

Robust Spiking Neural Networks Against Adversarial Attacks
by: Wang, Shuai, et al.
Published: (2026)

Backdoor Attack with Sparse and Invisible Trigger
by: Gao, Yinghua, et al.
Published: (2023)

Adaptive Surrogate Gradients for Sequential Reinforcement Learning in Spiking Neural Networks
by: Berghe, Korneel Van den, et al.
Published: (2025)

Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples
by: Xu, Nuo, et al.
Published: (2022)

Adversarially Robust Spiking Neural Networks with Sparse Connectivity
by: Schmolli, Mathias, et al.
Published: (2025)

Beyond Rate Coding: Surrogate Gradients Enable Spike Timing Learning in Spiking Neural Networks
by: Yu, Ziqiao, et al.
Published: (2025)

Invisible Adversaries: A Systematic Study of Session Manipulation Attacks on VPNs
by: Yang, Yuxiang, et al.
Published: (2026)

Towards Interpretable Adversarial Examples via Sparse Adversarial Attack
by: Lin, Fudong, et al.
Published: (2025)

Input-Specific and Universal Adversarial Attack Generation for Spiking Neural Networks in the Spiking Domain
by: Raptis, Spyridon, et al.
Published: (2025)

BadSNN: Backdoor Attacks on Spiking Neural Networks via Adversarial Spiking Neuron
by: Miah, Abdullah Arafat, et al.
Published: (2026)

Input-Specific and Universal Adversarial Attack Generation for Spiking Neural Networks in the Spiking Domain
by: Raptis, Spyridon, et al.
Published: (2025)

SpikingGamma: Surrogate-Gradient Free and Temporally Precise Online Training of Spiking Neural Networks with Smoothed Delays
by: Koopman, Roel, et al.
Published: (2026)

Fine-Tuning Surrogate Gradient Learning for Optimal Hardware Performance in Spiking Neural Networks
by: Aliyev, Ilkin, et al.
Published: (2024)

On the Privacy-Preserving Properties of Spiking Neural Networks with Unique Surrogate Gradients and Quantization Levels
by: Moshruba, Ayana, et al.
Published: (2025)

Towards Reliable Evaluation of Adversarial Robustness for Spiking Neural Networks
by: Wang, Jihang, et al.
Published: (2025)

Adversarial Vulnerabilities in Neural Operator Digital Twins: Gradient-Free Attacks on Nuclear Thermal-Hydraulic Surrogates
by: Roy, Samrendra, et al.
Published: (2026)

Sharpness Aware Surrogate Training for Spiking Neural Networks
by: Nicholson, Maximilian
Published: (2026)

MPD-SGR: Robust Spiking Neural Networks with Membrane Potential Distribution-Driven Surrogate Gradient Regularization
by: Jiang, Runhao, et al.
Published: (2025)

Stabilizing Direct Training of Spiking Neural Networks: Membrane Potential Initialization and Threshold-robust Surrogate Gradient
by: Kook, Hyunho, et al.
Published: (2025)

Spike-PTSD: A Bio-Plausible Adversarial Example Attack on Spiking Neural Networks via PTSD-Inspired Spike Scaling
by: Jin, Lingxin, et al.
Published: (2026)

Understanding the Robustness of Graph Neural Networks against Adversarial Attacks
by: Wu, Tao, et al.
Published: (2024)

SpikeX: Exploring Accelerator Architecture and Network-Hardware Co-Optimization for Sparse Spiking Neural Networks
by: Xu, Boxun, et al.
Published: (2025)

Towards Accurate Binary Spiking Neural Networks: Learning with Adaptive Gradient Modulation Mechanism
by: Liang, Yu, et al.
Published: (2025)

SURGE: Surrogate Gradient Adaptation in Binary Neural Networks
by: Huang, Haoyu, et al.
Published: (2026)

Workload-Balanced Pruning for Sparse Spiking Neural Networks
by: Yin, Ruokai, et al.
Published: (2023)

Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attacks
by: Zhang, Zongyuan, et al.
Published: (2025)

Hybrid Temporal-8-Bit Spike Coding for Spiking Neural Network Surrogate Training
by: Nhan, Luu Trong, et al.
Published: (2025)

Sharpness-Aware Surrogate Training for On-Sensor Spiking Neural Networks
by: Nicholson, Maximilian
Published: (2026)

Cannistraci-Hebb Training on Ultra-Sparse Spiking Neural Networks
by: Hua, Yuan, et al.
Published: (2025)

Spike Accumulation Forwarding for Effective Training of Spiking Neural Networks
by: Saiin, Ryuji, et al.
Published: (2023)

Adversarial Attacks on Fairness of Graph Neural Networks
by: Zhang, Binchi, et al.
Published: (2023)

Efficient and Effective Time-Series Forecasting with Spiking Neural Networks
by: Lv, Changze, et al.
Published: (2024)

Adversarial Attacks on Locally Private Graph Neural Networks
by: Varun, Matta, et al.
Published: (2026)

Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Data
by: Simonetto, Thibault, et al.
Published: (2024)

Optimization over Trained (and Sparse) Neural Networks: A Surrogate within a Surrogate
by: Pham, Hung, et al.
Published: (2025)

Late Breaking Results: Fortifying Neural Networks: Safeguarding Against Adversarial Attacks with Stochastic Computing
by: Banitaba, Faeze S., et al.
Published: (2024)

Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks
by: Feng, Yunzhen, et al.
Published: (2024)

HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds
by: Geng, Hejia, et al.
Published: (2023)