Imagen de Portada

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

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Xu, Nuo, Mahmood, Kaleel, Fang, Haowen, Rathbun, Ethan, Ding, Caiwen, Wen, Wujie
Formato:	Preprint
Publicado:	2022
Materias:	Neural and Evolutionary Computing Artificial Intelligence Cryptography and Security Computer Vision and Pattern Recognition Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

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

Data Poisoning-based Backdoor Attack Framework against Supervised Learning Rules of Spiking Neural Networks
por: Jin, Lingxin, et al.
Publicado: (2024)

Do Spikes Protect Privacy? Investigating Black-Box Model Inversion Attacks in Spiking Neural Networks
por: Poursiami, Hamed, et al.
Publicado: (2025)

On the Adversarial Robustness of Spiking Neural Networks Trained by Local Learning
por: Lin, Jiaqi, et al.
Publicado: (2025)

A Homomorphic Encryption Framework for Privacy-Preserving Spiking Neural Networks
por: Nikfam, Farzad, et al.
Publicado: (2023)

Watermarking Neuromorphic Brains: Intellectual Property Protection in Spiking Neural Networks
por: Poursiami, Hamed, et al.
Publicado: (2024)

Time-Distributed Backdoor Attacks on Federated Spiking Learning
por: Abad, Gorka, et al.
Publicado: (2024)

Impact of White-Box Adversarial Attacks on Convolutional Neural Networks
por: Podder, Rakesh, et al.
Publicado: (2024)

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

Rectifying Adversarial Examples Using Their Vulnerabilities
por: Morimoto, Fumiya, et al.
Publicado: (2026)

Temporal-Guided Spiking Neural Networks for Event-Based Human Action Recognition
por: Yang, Siyuan, et al.
Publicado: (2025)

MALT Powers Up Adversarial Attacks
por: Melamed, Odelia, et al.
Publicado: (2024)

Hybrid Deep Learning Model for Multiple Cache Side Channel Attacks Detection: A Comparative Analysis
por: Joshi, Tejal, et al.
Publicado: (2025)

Privacy-Preserving Distributed Optimization Under Time Constraints Using Secure Multi-Party Computation and Evolutionary Algorithms
por: Gruber, Sebastian, et al.
Publicado: (2026)

Proof-of-Useful-Work Blockchain for Trustworthy Biomedical Hyperdimensional Computing
por: Wen, Jinghao, et al.
Publicado: (2022)

Cross-Generational Transfer of Adversarial Attacks Reveals Non-Monotonic Safety Alignment in LLMs
por: Mitra, Subhadip
Publicado: (2026)

Adversarial Example Generation using Evolutionary Multi-objective Optimization
por: Suzuki, Takahiro, et al.
Publicado: (2019)

Boosting Adversarial Robustness and Generalization with Structural Prior
por: Hou, Zhichao, et al.
Publicado: (2025)

LLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasures
por: Aguilera-Martínez, Francisco, et al.
Publicado: (2025)

Direct-to-Event Spiking Neural Network Transfer
por: Luu, Nhan Trong, et al.
Publicado: (2026)

Monotone but Exciting: On Evolving Monotone Boolean Functions with High Nonlinearity
por: Carlet, Claude, et al.
Publicado: (2026)

Degree is Important: On Evolving Homogeneous Boolean Functions
por: Carlet, Claude, et al.
Publicado: (2025)

IDEM Enough? Evolving Highly Nonlinear Idempotent Boolean Functions
por: Carlet, Claude, et al.
Publicado: (2026)

A Systematic Evaluation of Evolving Highly Nonlinear Boolean Functions in Odd Sizes
por: Carlet, Claude, et al.
Publicado: (2024)

A Systematic Study on the Design of Odd-Sized Highly Nonlinear Boolean Functions via Evolutionary Algorithms
por: Carlet, Claude, et al.
Publicado: (2025)

NegaBent, No Regrets: Evolving Spectrally Flat Boolean Functions
por: Carlet, Claude, et al.
Publicado: (2026)

On Counts and Densities of Homogeneous Bent Functions: An Evolutionary Approach
por: Carlet, Claude, et al.
Publicado: (2025)

A Discrete Particle Swarm Optimizer for the Design of Cryptographic Boolean Functions
por: Mariot, Luca, et al.
Publicado: (2024)

Adapting Novelty towards Generating Antigens for Antivirus systems
por: Murali, Ritwik, et al.
Publicado: (2025)

A systematic review of metaheuristics-based and machine learning-driven intrusion detection systems in IoT
por: Ahsan, Mohammad Shamim, et al.
Publicado: (2025)

Provable Unlearning with Gradient Ascent on Two-Layer ReLU Neural Networks
por: Melamed, Odelia, et al.
Publicado: (2025)

From One Attack Domain to Another: Contrastive Transfer Learning with Siamese Networks for APT Detection
por: Benabderrahmane, Sidahmed, et al.
Publicado: (2025)

Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent
por: Kummer, Lorenz, et al.
Publicado: (2023)

Robust Stable Spiking Neural Networks
por: Ding, Jianhao, et al.
Publicado: (2024)

BrainLeaks: On the Privacy-Preserving Properties of Neuromorphic Architectures against Model Inversion Attacks
por: Poursiami, Hamed, et al.
Publicado: (2024)

Stochastic Spiking Neural Networks with First-to-Spike Coding
por: Jiang, Yi, et al.
Publicado: (2024)

Classifying Images with CoLaNET Spiking Neural Network -- the MNIST Example
por: Kiselev, Mikhail
Publicado: (2024)

Efficiently Training Time-to-First-Spike Spiking Neural Networks from Scratch
por: Che, Kaiwei, et al.
Publicado: (2024)

Graph Agent Network: Empowering Nodes with Inference Capabilities for Adversarial Resilience
por: Liu, Ao, et al.
Publicado: (2023)

Enhancing IoT Security: A Novel Feature Engineering Approach for ML-Based Intrusion Detection Systems
por: Mahanipour, Afsaneh, et al.
Publicado: (2024)