Copertina

On the Robustness of Bayesian Neural Networks to Adversarial Attacks

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Bortolussi, Luca, Carbone, Ginevra, Laurenti, Luca, Patane, Andrea, Sanguinetti, Guido, Wicker, Matthew
Natura:	Preprint
Pubblicazione:	2022
Soggetti:	Machine Learning Artificial Intelligence Cryptography and Security
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Frequency maps reveal the correlation between Adversarial Attacks and Implicit Bias
di: Basile, Lorenzo, et al.
Pubblicazione: (2023)

Blending adversarial training and representation-conditional purification via aggregation improves adversarial robustness
di: Ballarin, Emanuele, et al.
Pubblicazione: (2023)

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

Optimal Transport-Guided Adversarial Attacks on Graph Neural Network-Based Bot Detection
di: Mukherjee, Kunal, et al.
Pubblicazione: (2026)

Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks
di: Abbahaddou, Yassine, et al.
Pubblicazione: (2024)

Unlearning Inversion Attacks for Graph Neural Networks
di: Zhang, Jiahao, et al.
Pubblicazione: (2025)

UNDREAM: Bridging Differentiable Rendering and Photorealistic Simulation for End-to-end Adversarial Attacks
di: Phute, Mansi, et al.
Pubblicazione: (2025)

Are Robust LLM Fingerprints Adversarially Robust?
di: Nasery, Anshul, et al.
Pubblicazione: (2025)

Adversarial Attacks on Transformers-Based Malware Detectors
di: Jakhotiya, Yash, et al.
Pubblicazione: (2022)

Untargeted Adversarial Attack on Knowledge Graph Embeddings
di: Zhao, Tianzhe, et al.
Pubblicazione: (2024)

Relationship between Uncertainty in DNNs and Adversarial Attacks
di: Ogonna, Mabel, et al.
Pubblicazione: (2024)

Black-box Adversarial Attacks on Network-wide Multi-step Traffic State Prediction Models
di: Poudel, Bibek, et al.
Pubblicazione: (2021)

Verification of Bit-Flip Attacks against Quantized Neural Networks
di: Zhang, Yedi, et al.
Pubblicazione: (2025)

Backdoor Attack on Vertical Federated Graph Neural Network Learning
di: Yang, Jirui, et al.
Pubblicazione: (2024)

Vision Transformer with Adversarial Indicator Token against Adversarial Attacks in Radio Signal Classifications
di: Zhang, Lu, et al.
Pubblicazione: (2025)

Quantifying the Noise of Structural Perturbations on Graph Adversarial Attacks
di: Fang, Junyuan, et al.
Pubblicazione: (2025)

A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks
di: Papadopoulos, Georgios, et al.
Pubblicazione: (2025)

Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning
di: Lee, Sunwoo, et al.
Pubblicazione: (2025)

Disttack: Graph Adversarial Attacks Toward Distributed GNN Training
di: Zhang, Yuxiang, et al.
Pubblicazione: (2024)

Investigating Imperceptibility of Adversarial Attacks on Tabular Data: An Empirical Analysis
di: He, Zhipeng, et al.
Pubblicazione: (2024)

Investigating the Impact of Quantization on Adversarial Robustness
di: Li, Qun, et al.
Pubblicazione: (2024)

TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networks
di: Hossain, Khondoker Murad, et al.
Pubblicazione: (2024)

CARACAS: vehiCular ArchitectuRe for detAiled Can Attacks Simulation
di: Kirdi, Sadek Misto, et al.
Pubblicazione: (2024)

IoT-based Android Malware Detection Using Graph Neural Network With Adversarial Defense
di: Yumlembam, Rahul, et al.
Pubblicazione: (2025)

A White-Box Adversarial Attack Against a Digital Twin
di: Patterson, Wilson, et al.
Pubblicazione: (2022)

MF-CLIP: Leveraging CLIP as Surrogate Models for No-box Adversarial Attacks
di: Zhang, Jiaming, et al.
Pubblicazione: (2023)

Claudini: Autoresearch Discovers State-of-the-Art Adversarial Attack Algorithms for LLMs
di: Panfilov, Alexander, et al.
Pubblicazione: (2026)

Practical Adversarial Attacks on Stochastic Bandits via Fake Data Injection
di: Zeng, Qirun, et al.
Pubblicazione: (2025)

Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients
di: Yuan, Jinsheng, et al.
Pubblicazione: (2025)

Runtime Detection of Adversarial Attacks in AI Accelerators Using Performance Counters
di: Rahaman, Habibur, et al.
Pubblicazione: (2025)

AGSOA:Graph Neural Network Targeted Attack Based on Average Gradient and Structure Optimization
di: Chen, Yang, et al.
Pubblicazione: (2024)

Attacking Byzantine Robust Aggregation in High Dimensions
di: Choudhary, Sarthak, et al.
Pubblicazione: (2023)

The Dark Side of Digital Twins: Adversarial Attacks on AI-Driven Water Forecasting
di: Homaei, Mohammadhossein, et al.
Pubblicazione: (2025)

Jailbreaking GPT-4V via Self-Adversarial Attacks with System Prompts
di: Wu, Yuanwei, et al.
Pubblicazione: (2023)

MoCo-EA: Exploiting Adversarial Mode Connectivity for Efficient Evolutionary Attacks
di: Kim, Hyo Seo, et al.
Pubblicazione: (2026)

Counter-Samples: A Stateless Strategy to Neutralize Black Box Adversarial Attacks
di: Bokobza, Roey, et al.
Pubblicazione: (2024)

ReVeil: Unconstrained Concealed Backdoor Attack on Deep Neural Networks using Machine Unlearning
di: Alam, Manaar, et al.
Pubblicazione: (2025)

Variational Randomized Smoothing for Sample-Wise Adversarial Robustness
di: Hase, Ryo, et al.
Pubblicazione: (2024)

A General Black-box Adversarial Attack on Graph-based Fake News Detectors
di: Zhu, Peican, et al.
Pubblicazione: (2024)

Medusa: Cross-Modal Transferable Adversarial Attacks on Multimodal Medical Retrieval-Augmented Generation
di: Shang, Yingjia, et al.
Pubblicazione: (2025)