:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Brama, Haya, Dery, Lihi, Grinshpoun, Tal
Natura:	Preprint
Pubblicazione:	2022
Soggetti:	Cryptography and Security Computer Vision and Pattern Recognition Machine Learning Neural and Evolutionary Computing
Accesso online:	https://arxiv.org/abs/2201.05071
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

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

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

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

Protecting Feed-Forward Networks from Adversarial Attacks Using Predictive Coding
di: Ganjidoost, Ehsan, et al.
Pubblicazione: (2024)

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

A Theoretical View of Linear Backpropagation and Its Convergence
di: Li, Ziang, et al.
Pubblicazione: (2021)

Adversarial Robustness on Image Classification with $k$-means
di: Omari, Rollin, et al.
Pubblicazione: (2023)

Enhancing Adversarial Robustness in SNNs with Sparse Gradients
di: Liu, Yujia, et al.
Pubblicazione: (2024)

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

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

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

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

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

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

Use of Graph Neural Networks in Aiding Defensive Cyber Operations
di: Mitra, Shaswata, et al.
Pubblicazione: (2024)

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

Timestep-Compressed Attack on Spiking Neural Networks through Timestep-Level Backpropagation
di: Kang, Donghwa, et al.
Pubblicazione: (2025)

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

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

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

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

MALT Powers Up Adversarial Attacks
di: Melamed, Odelia, et al.
Pubblicazione: (2024)

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

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

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

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

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

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

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

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

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

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

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

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

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

Multi-Task Neural Architecture Search Using Architecture Embedding and Transfer Rank
di: Zhang, TingJie, et al.
Pubblicazione: (2025)

From Pixels to Titles: Video Game Identification by Screenshots using Convolutional Neural Networks
di: Breve, Fabricio
Pubblicazione: (2023)

Metric as Transform: Exploring beyond Affine Transform for Interpretable Neural Network
di: Sapkota, Suman
Pubblicazione: (2024)

GazeSCRNN: Event-based Near-eye Gaze Tracking using a Spiking Neural Network
di: Groenen, Stijn, et al.
Pubblicazione: (2025)