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Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network

1 October 2018

Papers citing "Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network"

50 / 57 papers shown

Title
Feature Statistics with Uncertainty Help Adversarial Robustness Ran A. Wang Xinlei Zhou Meng Hu Rihao Li Wenhui Wu Yuheng Jia AAML 132 0 0 26 Mar 2025
Inferring the Langevin Equation with Uncertainty via Bayesian Neural Networks Youngkyoung Bae Seungwoong Ha Hawoong Jeong 112 2 0 02 Feb 2024
Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models Weikang Yu Yonghao Xu Pedram Ghamisi 108 4 0 31 Jul 2023
Post-train Black-box Defense via Bayesian Boundary Correction He Wang Yunfeng Diao AAML 89 1 0 29 Jun 2023
Individual Fairness in Bayesian Neural Networks Alice Doherty Matthew Wicker Luca Laurenti A. Patané 147 5 0 21 Apr 2023
Detection of Uncertainty in Exceedance of Threshold (DUET): An Adversarial Patch Localizer Terence Jie Chua Wen-li Yu Junfeng Zhao AAML UQCV 69 1 0 18 Mar 2023
Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples Qizhang Li Yiwen Guo W. Zuo Hao Chen AAML 127 37 0 10 Feb 2023
DISCO: Adversarial Defense with Local Implicit Functions Chih-Hui Ho Nuno Vasconcelos AAML 130 39 0 11 Dec 2022
Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense Bao Gia Doan Ehsan Abbasnejad Javen Qinfeng Shi Damith Ranashinghe AAML OOD 87 8 0 05 Dec 2022
Unifying Gradients to Improve Real-world Robustness for Deep Networks Yingwen Wu Sizhe Chen Kun Fang Xiaolin Huang AAML 88 3 0 12 Aug 2022
Attacking Adversarial Defences by Smoothing the Loss Landscape Panagiotis Eustratiadis Henry Gouk Da Li Timothy M. Hospedales AAML 89 4 0 01 Aug 2022
Adversarial Example Detection in Deployed Tree Ensembles Laurens Devos Wannes Meert Jesse Davis AAML 56 1 0 27 Jun 2022
Demystifying the Adversarial Robustness of Random Transformation Defenses Chawin Sitawarin Zachary Golan-Strieb David Wagner AAML 94 21 0 18 Jun 2022
An Eye for an Eye: Defending against Gradient-based Attacks with Gradients Hanbin Hong Yuan Hong Yu Kong AAML 67 2 0 02 Feb 2022
A Review of Adversarial Attack and Defense for Classification Methods Yao Li Minhao Cheng Cho-Jui Hsieh T. C. Lee AAML 76 69 0 18 Nov 2021
Defensive Tensorization Adrian Bulat Jean Kossaifi S. Bhattacharya Yannis Panagakis Timothy M. Hospedales Georgios Tzimiropoulos Nicholas D. Lane Maja Pantic AAML 37 4 0 26 Oct 2021
Fast Gradient Non-sign Methods Yaya Cheng Jingkuan Song Xiaosu Zhu Qilong Zhang Lianli Gao Heng Tao Shen AAML 125 11 0 25 Oct 2021
Advances in adversarial attacks and defenses in computer vision: A survey Naveed Akhtar Ajmal Mian Navid Kardan M. Shah AAML 168 242 0 01 Aug 2021
GradDiv: Adversarial Robustness of Randomized Neural Networks via Gradient Diversity Regularization Sungyoon Lee Hoki Kim Jaewook Lee AAML 84 55 0 06 Jul 2021
Being a Bit Frequentist Improves Bayesian Neural Networks Agustinus Kristiadi Matthias Hein Philipp Hennig BDL UQCV 95 16 0 18 Jun 2021
Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart Tianyu Pang Huishuai Zhang Di He Yinpeng Dong Hang Su Wei Chen Jun Zhu Tie-Yan Liu AAML 52 18 0 31 May 2021
Adversarial Examples Detection with Bayesian Neural Network Yao Li Tongyi Tang Cho-Jui Hsieh T. C. Lee GAN AAML 75 3 0 18 May 2021
Relating Adversarially Robust Generalization to Flat Minima David Stutz Matthias Hein Bernt Schiele OOD 105 67 0 09 Apr 2021
Bayesian Inference with Certifiable Adversarial Robustness Matthew Wicker Luca Laurenti A. Patané Zhoutong Chen Zheng Zhang Marta Z. Kwiatkowska AAML BDL 142 30 0 10 Feb 2021
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation Wuxinlin Cheng Chenhui Deng Zhiqiang Zhao Yaohui Cai Zhiru Zhang Zhuo Feng AAML 86 14 0 07 Feb 2021
Bayesian Inference Forgetting Shaopeng Fu Fengxiang He Yue Xu Dacheng Tao MU 88 12 0 16 Jan 2021
The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks Arno Blaas Stephen J. Roberts BDL AAML 90 2 0 07 Jan 2021
A Simple Fine-tuning Is All You Need: Towards Robust Deep Learning Via Adversarial Fine-tuning Ahmadreza Jeddi M. Shafiee A. Wong AAML 84 40 0 25 Dec 2020
Self-Progressing Robust Training Minhao Cheng Pin-Yu Chen Sijia Liu Shiyu Chang Cho-Jui Hsieh Payel Das AAML VLM 74 9 0 22 Dec 2020
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges Moloud Abdar Farhad Pourpanah Sadiq Hussain Dana Rezazadegan Li Liu ... Xiaochun Cao Abbas Khosravi U. Acharya V. Makarenkov S. Nahavandi BDL UQCV 382 1,952 0 12 Nov 2020
Weight-Covariance Alignment for Adversarially Robust Neural Networks Panagiotis Eustratiadis Henry Gouk Da Li Timothy M. Hospedales OOD AAML 86 23 0 17 Oct 2020
A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning Hongjun Wang Guanbin Li Xiaobai Liu Liang Lin GAN AAML 95 23 0 15 Oct 2020
Torchattacks: A PyTorch Repository for Adversarial Attacks Hoki Kim 77 208 0 24 Sep 2020
Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective G. R. Machado Eugênio Silva R. Goldschmidt AAML 136 164 0 08 Sep 2020
Rethinking Non-idealities in Memristive Crossbars for Adversarial Robustness in Neural Networks Abhiroop Bhattacharjee Priyadarshini Panda AAML 67 19 0 25 Aug 2020
Investigating maximum likelihood based training of infinite mixtures for uncertainty quantification Sina Daubener Asja Fischer BDL UQCV 63 2 0 07 Aug 2020
Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification Sina Daubener Lea Schonherr Asja Fischer D. Kolossa AAML 71 18 0 24 May 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 120 382 0 30 Apr 2020
Probabilistic Safety for Bayesian Neural Networks Matthew Wicker Luca Laurenti A. Patané Marta Z. Kwiatkowska AAML 66 52 0 21 Apr 2020
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness Ahmadreza Jeddi M. Shafiee Michelle Karg C. Scharfenberger A. Wong OOD AAML 129 67 0 02 Mar 2020
Adversarial Ranking Attack and Defense Mo Zhou Zhenxing Niu Le Wang Qilin Zhang G. Hua 156 39 0 26 Feb 2020
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks Agustinus Kristiadi Matthias Hein Philipp Hennig BDL UQCV 90 290 0 24 Feb 2020
Robustness of Bayesian Neural Networks to Gradient-Based Attacks Ginevra Carbone Matthew Wicker Luca Laurenti A. Patané Luca Bortolussi G. Sanguinetti AAML 106 79 0 11 Feb 2020
Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification Meet P. Vadera Satya Narayan Shukla B. Jalaeian Benjamin M. Marlin AAML BDL 45 6 0 07 Feb 2020
Benchmarking Adversarial Robustness Yinpeng Dong Qi-An Fu Xiao Yang Tianyu Pang Hang Su Zihao Xiao Jun Zhu AAML 108 36 0 26 Dec 2019
Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing Jinyuan Jia Xiaoyu Cao Binghui Wang Neil Zhenqiang Gong AAML 62 96 0 20 Dec 2019
Analysis of Deep Networks for Monocular Depth Estimation Through Adversarial Attacks with Proposal of a Defense Method Junjie Hu Takayuki Okatani AAML MDE 67 17 0 20 Nov 2019
Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks David Stutz Matthias Hein Bernt Schiele AAML 89 5 0 14 Oct 2019
Universal Physical Camouflage Attacks on Object Detectors Lifeng Huang Chengying Gao Yuyin Zhou Cihang Xie Alan Yuille C. Zou Ning Liu AAML 182 169 0 10 Sep 2019
Comment on "Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network" Roland S. Zimmermann AAML 73 24 0 01 Jul 2019