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Adversarial Machine Learning at Scale

4 November 2016

Papers citing "Adversarial Machine Learning at Scale"

50 / 1,610 papers shown

Title
Backpropagating Linearly Improves Transferability of Adversarial Examples Yiwen Guo Qizhang Li Hao Chen FedML AAML 84 117 0 07 Dec 2020
Practical No-box Adversarial Attacks against DNNs Qizhang Li Yiwen Guo Hao Chen AAML 79 59 0 04 Dec 2020
FAT: Federated Adversarial Training Giulio Zizzo Ambrish Rawat M. Sinn Beat Buesser FedML 89 43 0 03 Dec 2020
Content-Adaptive Pixel Discretization to Improve Model Robustness Ryan Feng Wu-chi Feng Atul Prakash AAML 37 0 0 03 Dec 2020
$Towards Defending Multiple $\ell_p$-norm Bounded Adversarial Perturbations via Gated Batch Normalization$ Towards Defending Multiple $\ell_p$ -norm Bounded Adversarial Perturbations via Gated Batch Normalization Aishan Liu Shiyu Tang Xinyun Chen Lei Huang Zhuozhuo Tu Xianglong Liu Dacheng Tao AAML 110 35 0 03 Dec 2020
From a Fourier-Domain Perspective on Adversarial Examples to a Wiener Filter Defense for Semantic Segmentation Nikhil Kapoor Andreas Bär Serin Varghese Jan David Schneider Fabian Hüger Peter Schlicht Tim Fingscheidt AAML 74 10 0 02 Dec 2020
Visually Imperceptible Adversarial Patch Attacks on Digital Images Yaguan Qian Jiamin Wang Bin Wang Xiang Ling Zhaoquan Gu Chunming Wu Wassim Swaileh AAML 66 2 0 02 Dec 2020
Boosting Adversarial Attacks on Neural Networks with Better Optimizer Heng Yin Hengwei Zhang Jin-dong Wang Ruiyu Dou AAML 76 8 0 01 Dec 2020
Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses Gaurang Sriramanan Sravanti Addepalli Arya Baburaj R. Venkatesh Babu AAML 90 95 0 30 Nov 2020
FaceGuard: A Self-Supervised Defense Against Adversarial Face Images Debayan Deb Xiaoming Liu Anil K. Jain CVBM AAML PICV 98 27 0 28 Nov 2020
Deterministic Certification to Adversarial Attacks via Bernstein Polynomial Approximation Ching-Chia Kao Jhe-Bang Ko Chun-Shien Lu AAML 65 1 0 28 Nov 2020
Voting based ensemble improves robustness of defensive models Devvrit Minhao Cheng Cho-Jui Hsieh Inderjit Dhillon OOD FedML AAML 73 12 0 28 Nov 2020
A Study on the Uncertainty of Convolutional Layers in Deep Neural Networks Hao Shen Sihong Chen Ran Wang 70 5 0 27 Nov 2020
Use the Spear as a Shield: A Novel Adversarial Example based Privacy-Preserving Technique against Membership Inference Attacks Mingfu Xue Chengxiang Yuan Can He Zhiyu Wu Yushu Zhang Yanfeng Guo Weiqiang Liu MIACV 16 12 0 27 Nov 2020
Delving Deep into Label Smoothing Chang-Bin Zhang Peng-Tao Jiang Qibin Hou Yunchao Wei Qi Han Zhen Li Ming-Ming Cheng 134 215 0 25 Nov 2020
When Machine Learning Meets Privacy: A Survey and Outlook B. Liu Ming Ding Sina shaham W. Rahayu F. Farokhi Zihuai Lin 97 293 0 24 Nov 2020
Nudge Attacks on Point-Cloud DNNs Yiren Zhao Ilia Shumailov Robert D. Mullins Ross J. Anderson 3DPC AAML 57 9 0 22 Nov 2020
Spatially Correlated Patterns in Adversarial Images Nandish Chattopadhyay Lionell Yip En Zhi Bryan Tan Bing Xing Anupam Chattopadhyay AAML 41 2 0 21 Nov 2020
Contextual Fusion For Adversarial Robustness Aiswarya Akumalla S. Haney M. Bazhenov AAML 36 1 0 18 Nov 2020
Challenges in Deploying Machine Learning: a Survey of Case Studies Andrei Paleyes Raoul-Gabriel Urma Neil D. Lawrence 71 409 0 18 Nov 2020
Self-Gradient Networks Hossein Aboutalebi M. Shafiee AAML 29 0 0 18 Nov 2020
Shaping Deep Feature Space towards Gaussian Mixture for Visual Classification Weitao Wan Jiansheng Chen Cheng Yu Tong Wu Yuanyi Zhong Ming-Hsuan Yang 38 8 0 18 Nov 2020
Probing Predictions on OOD Images via Nearest Categories Yao-Yuan Yang Cyrus Rashtchian Ruslan Salakhutdinov Kamalika Chaudhuri AAML 79 0 0 17 Nov 2020
FoolHD: Fooling speaker identification by Highly imperceptible adversarial Disturbances Ali Shahin Shamsabadi Francisco Teixeira A. Abad Bhiksha Raj Andrea Cavallaro Isabel Trancoso AAML 62 30 0 17 Nov 2020
Extreme Value Preserving Networks Mingjie Sun Jianguo Li Changshui Zhang AAML MDE 33 0 0 17 Nov 2020
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks Yuxin Wen Shuai Li Kui Jia AAML 72 24 0 15 Nov 2020
Bridging the Performance Gap between FGSM and PGD Adversarial Training Tianjin Huang Vlado Menkovski Yulong Pei Mykola Pechenizkiy AAML 46 20 0 07 Nov 2020
A Black-Box Attack Model for Visually-Aware Recommender Systems Rami Cohen Oren Sar Shalom Dietmar Jannach A. Amir 50 28 0 05 Nov 2020
The Vulnerability of the Neural Networks Against Adversarial Examples in Deep Learning Algorithms Rui Zhao AAML 69 1 0 02 Nov 2020
WaveTransform: Crafting Adversarial Examples via Input Decomposition Divyam Anshumaan Akshay Agarwal Mayank Vatsa Richa Singh AAML 54 11 0 29 Oct 2020
Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples Yingwei Li Zhuotun Zhu Yuyin Zhou Yingda Xia Wei Shen Elliot K. Fishman Alan Yuille MedIm 87 23 0 29 Oct 2020
A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems Jiawei Zhang Peijun Xiao Ruoyu Sun Zhi-Quan Luo 128 99 0 29 Oct 2020
GreedyFool: Distortion-Aware Sparse Adversarial Attack Xiaoyi Dong Dongdong Chen Jianmin Bao Chuan Qin Lu Yuan Weiming Zhang Nenghai Yu Dong Chen AAML 72 63 0 26 Oct 2020
Attack Agnostic Adversarial Defense via Visual Imperceptible Bound S. Chhabra Akshay Agarwal Richa Singh Mayank Vatsa AAML 66 3 0 25 Oct 2020
Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks Huimin Zeng Chen Zhu Tom Goldstein Furong Huang AAML 74 18 0 24 Oct 2020
Towards Robust Neural Networks via Orthogonal Diversity Kun Fang Qinghua Tao Yingwen Wu Tao Li Jia Cai Feipeng Cai Xiaolin Huang Jie Yang AAML 101 8 0 23 Oct 2020
Contrastive Learning with Adversarial Examples Chih-Hui Ho Nuno Vasconcelos SSL 92 142 0 22 Oct 2020
Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks Shenhao Wang Baichuan Mo Jinhuan Zhao AI4CE 48 36 0 22 Oct 2020
Precise Statistical Analysis of Classification Accuracies for Adversarial Training Adel Javanmard Mahdi Soltanolkotabi AAML 110 63 0 21 Oct 2020
Certified Distributional Robustness on Smoothed Classifiers Jungang Yang Liyao Xiang Pengzhi Chu Yukun Wang Cheng Zhou Xinbing Wang AAML 51 0 0 21 Oct 2020
Learning Black-Box Attackers with Transferable Priors and Query Feedback Jiancheng Yang Yangzhou Jiang Xiaoyang Huang Bingbing Ni Chenglong Zhao AAML 135 82 0 21 Oct 2020
Boosting Gradient for White-Box Adversarial Attacks Hongying Liu Zhenyu Zhou Fanhua Shang Xiaoyu Qi Yuanyuan Liu L. Jiao AAML 56 8 0 21 Oct 2020
Ulixes: Facial Recognition Privacy with Adversarial Machine Learning Thomas Cilloni Wei Wang Charles Walter Charles Fleming PICV AAML 44 9 0 20 Oct 2020
Poisoned classifiers are not only backdoored, they are fundamentally broken Mingjie Sun Siddhant Agarwal J. Zico Kolter 61 26 0 18 Oct 2020
Exploiting Vulnerabilities of Deep Learning-based Energy Theft Detection in AMI through Adversarial Attacks Jiangnan Li Yingyuan Yang Jinyuan Stella Sun AAML 81 8 0 16 Oct 2020
Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness Long Zhao Ting Liu Xi Peng Dimitris N. Metaxas OOD AAML 131 172 0 15 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
Survive the Schema Changes: Integration of Unmanaged Data Using Deep Learning Zijie Wang Lixi Zhou A. Das Valay Dave Zhanpeng Jin Jia Zou 90 3 0 15 Oct 2020
Linking average- and worst-case perturbation robustness via class selectivity and dimensionality Matthew L. Leavitt Ari S. Morcos AAML 62 2 0 14 Oct 2020
To be Robust or to be Fair: Towards Fairness in Adversarial Training Han Xu Xiaorui Liu Yaxin Li Anil K. Jain Jiliang Tang 76 182 0 13 Oct 2020