Cover Image

Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Feng, Yunzhen, Rudner, Tim G. J., Tsilivis, Nikolaos, Kempe, Julia
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors
by: Rudner, Tim G. J., et al.
Published: (2024)

Embedding Trust: Semantic Isotropy Predicts Nonfactuality in Long-Form Text Generation
by: Bhardwaj, Dhrupad, et al.
Published: (2025)

ECG-IMN: Interpretable Mesomorphic Neural Networks for 12-Lead Electrocardiogram Interpretation
by: Thambawita, Vajira, et al.
Published: (2026)

Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution
by: Wang, Ying, et al.
Published: (2023)

Understanding Model Calibration -- A gentle introduction and visual exploration of calibration and the expected calibration error (ECE)
by: Pavlovic, Maja
Published: (2025)

Regularizing Attention Scores with Bootstrapping
by: Chung, Neo Christopher, et al.
Published: (2026)

On Pitfalls of $\textit{RemOve-And-Retrain}$: Data Processing Inequality Perspective
by: Song, Junhwa, et al.
Published: (2023)

Tailoring Adversarial Attacks on Deep Neural Networks for Targeted Class Manipulation Using DeepFool Algorithm
by: Labib, S. M. Fazle Rabby, et al.
Published: (2023)

CausAdv: A Causal-based Framework for Detecting Adversarial Examples
by: Debbi, Hichem
Published: (2024)

Explaining Bayesian Neural Networks
by: Bykov, Kirill, et al.
Published: (2021)

rSDNet: Unified Robust Neural Learning against Label Noise and Adversarial Attacks
by: Jana, Suryasis, et al.
Published: (2026)

Model Collapse Demystified: The Case of Regression
by: Dohmatob, Elvis, et al.
Published: (2024)

FACL-Attack: Frequency-Aware Contrastive Learning for Transferable Adversarial Attacks
by: Yang, Hunmin, et al.
Published: (2024)

Pixel-Based Similarities as an Alternative to Neural Data for Improving Convolutional Neural Network Adversarial Robustness
by: Attias, Elie, et al.
Published: (2024)

Robustness Tokens: Towards Adversarial Robustness of Transformers
by: Pulfer, Brian, et al.
Published: (2025)

Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networks
by: Doan, Bao Gia, et al.
Published: (2024)

On the Interaction of Compressibility and Adversarial Robustness
by: Barsbey, Melih, et al.
Published: (2025)

eXIAA: eXplainable Injections for Adversarial Attack
by: Pesce, Leonardo, et al.
Published: (2025)

Impact of Adversarial Attacks on Deep Learning Model Explainability
by: Nur, Gazi Nazia, et al.
Published: (2024)

The Anatomy of Adversarial Attacks: Concept-based XAI Dissection
by: Mikriukov, Georgii, et al.
Published: (2024)

Adversarial Attacks Leverage Interference Between Features in Superposition
by: Stevinson, Edward, et al.
Published: (2025)

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks
by: Xie, Yong, et al.
Published: (2024)

The Price of Implicit Bias in Adversarially Robust Generalization
by: Tsilivis, Nikolaos, et al.
Published: (2024)

Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition
by: Kong, Weizhe, et al.
Published: (2025)

SORA: Free Second-Order Attacks in Fast Adversarial Training
by: Teymourian, Mazdak, et al.
Published: (2026)

Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function Prior
by: Cheng, Shuyu, et al.
Published: (2024)

Hard-label based Small Query Black-box Adversarial Attack
by: Park, Jeonghwan, et al.
Published: (2024)

ADBA:Approximation Decision Boundary Approach for Black-Box Adversarial Attacks
by: Wang, Feiyang, et al.
Published: (2024)

QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits
by: Azimi, Navid, et al.
Published: (2026)

Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss
by: Yu, Yunrui, et al.
Published: (2025)

Hidden in Plain Sight: Undetectable Adversarial Bias Attacks on Vulnerable Patient Populations
by: Kulkarni, Pranav, et al.
Published: (2024)

Memory Efficient Full-gradient Attacks (MEFA) Framework for Adversarial Defense Evaluations
by: Du, Yuan, et al.
Published: (2026)

Language-Driven Anchors for Zero-Shot Adversarial Robustness
by: Li, Xiao, et al.
Published: (2023)

B-SMALL: A Bayesian Neural Network approach to Sparse Model-Agnostic Meta-Learning
by: Madan, Anish, et al.
Published: (2021)

Test-Time Defense Against Adversarial Attacks via Stochastic Resonance of Latent Ensembles
by: Lao, Dong, et al.
Published: (2025)

BankTweak: Adversarial Attack against Multi-Object Trackers by Manipulating Feature Banks
by: Shin, Woojin, et al.
Published: (2024)

IGAN: Inferent and Generative Adversarial Networks
by: Vignaud, Luc
Published: (2021)

Text-To-Image with Generative Adversarial Networks
by: Momen-Tayefeh, Mehrshad
Published: (2024)

Learning Robust Convolutional Neural Networks with Relevant Feature Focusing via Explanations
by: Adachi, Kazuki, et al.
Published: (2022)

ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object
by: Zhang, Chenshuang, et al.
Published: (2024)