Certified Robustness to Adversarial Examples with Differential Privacy

9 February 2018

Papers citing "Certified Robustness to Adversarial Examples with Differential Privacy"

50 / 208 papers shown

Title
Trustworthy AI: From Principles to Practices Bo-wen Li Peng Qi Bo Liu Shuai Di Jingen Liu Jiquan Pei Jinfeng Yi Bowen Zhou 119 356 0 04 Oct 2021
Local Intrinsic Dimensionality Signals Adversarial Perturbations Sandamal Weerasinghe T. Alpcan S. Erfani C. Leckie Benjamin I. P. Rubinstein AAML 23 0 0 24 Sep 2021
CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks Mikhail Aleksandrovich Pautov Nurislam Tursynbek Marina Munkhoeva Nikita Muravev Aleksandr Petiushko Ivan Oseledets AAML 52 16 0 22 Sep 2021
An automatic differentiation system for the age of differential privacy Dmitrii Usynin Alexander Ziller Moritz Knolle Andrew Trask Kritika Prakash Daniel Rueckert Georgios Kaissis 30 3 0 22 Sep 2021
PatchCleanser: Certifiably Robust Defense against Adversarial Patches for Any Image Classifier Chong Xiang Saeed Mahloujifar Prateek Mittal VLM AAML 24 73 0 20 Aug 2021
Advances in adversarial attacks and defenses in computer vision: A survey Naveed Akhtar Ajmal Mian Navid Kardan M. Shah AAML 31 236 0 01 Aug 2021
On the Certified Robustness for Ensemble Models and Beyond Zhuolin Yang Linyi Li Xiaojun Xu B. Kailkhura Tao Xie Bo-wen Li AAML 29 48 0 22 Jul 2021
ROPUST: Improving Robustness through Fine-tuning with Photonic Processors and Synthetic Gradients Alessandro Cappelli Julien Launay Laurent Meunier Ruben Ohana Iacopo Poli AAML 24 4 0 06 Jul 2021
Smoothed Differential Privacy Ao Liu Yu-Xiang Wang Lirong Xia 33 0 0 04 Jul 2021
Scalable Certified Segmentation via Randomized Smoothing Marc Fischer Maximilian Baader Martin Vechev 18 38 0 01 Jul 2021
Certified Robustness via Randomized Smoothing over Multiplicative Parameters of Input Transformations Nikita Muravev Aleksandr Petiushko AAML 18 7 0 28 Jun 2021
Policy Smoothing for Provably Robust Reinforcement Learning Aounon Kumar Alexander Levine S. Feizi AAML 20 56 0 21 Jun 2021
Adversarial Training Helps Transfer Learning via Better Representations Zhun Deng Linjun Zhang Kailas Vodrahalli Kenji Kawaguchi James Zou GAN 36 53 0 18 Jun 2021
Large Scale Private Learning via Low-rank Reparametrization Da Yu Huishuai Zhang Wei Chen Jian Yin Tie-Yan Liu 26 100 0 17 Jun 2021
Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks Yulong Cao* Ningfei Wang* Chaowei Xiao Dawei Yang Jin Fang Ruigang Yang Qi Alfred Chen Mingyan D. Liu Bo-wen Li AAML 24 218 0 17 Jun 2021
CRFL: Certifiably Robust Federated Learning against Backdoor Attacks Chulin Xie Minghao Chen Pin-Yu Chen Bo-wen Li FedML 36 165 0 15 Jun 2021
Adversarial Robustness via Fisher-Rao Regularization Marine Picot Francisco Messina Malik Boudiaf Fabrice Labeau Ismail Ben Ayed Pablo Piantanida AAML 28 23 0 12 Jun 2021
NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels Jingfeng Zhang Xilie Xu Bo Han Tongliang Liu Gang Niu Li-zhen Cui Masashi Sugiyama NoLa AAML 23 9 0 31 May 2021
Random Noise Defense Against Query-Based Black-Box Attacks Zeyu Qin Yanbo Fan H. Zha Baoyuan Wu AAML 27 59 0 23 Apr 2021
Adversarial Training is Not Ready for Robot Learning Mathias Lechner Ramin Hasani Radu Grosu Daniela Rus T. Henzinger AAML 38 34 0 15 Mar 2021
Membership Inference Attacks on Machine Learning: A Survey Hongsheng Hu Z. Salcic Lichao Sun Gillian Dobbie Philip S. Yu Xuyun Zhang MIACV 35 412 0 14 Mar 2021
DP-Image: Differential Privacy for Image Data in Feature Space Hanyu Xue Bo Liu Ming Ding Tianqing Zhu Dayong Ye Li-Na Song Wanlei Zhou 17 33 0 12 Mar 2021
PRIMA: General and Precise Neural Network Certification via Scalable Convex Hull Approximations Mark Niklas Muller Gleb Makarchuk Gagandeep Singh Markus Püschel Martin Vechev 41 90 0 05 Mar 2021
Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack Mengting Xu Tao Zhang Zhongnian Li Mingxia Liu Daoqiang Zhang AAML OOD MedIm 33 41 0 05 Mar 2021
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness Jacob D. Abernethy Pranjal Awasthi Satyen Kale AAML 27 6 0 01 Mar 2021
Globally-Robust Neural Networks Klas Leino Zifan Wang Matt Fredrikson AAML OOD 80 126 0 16 Feb 2021
Low Curvature Activations Reduce Overfitting in Adversarial Training Vasu Singla Sahil Singla David Jacobs S. Feizi AAML 32 45 0 15 Feb 2021
ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models Yugeng Liu Rui Wen Xinlei He A. Salem Zhikun Zhang Michael Backes Emiliano De Cristofaro Mario Fritz Yang Zhang AAML 17 125 0 04 Feb 2021
DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning Olakunle Ibitoye M. O. Shafiq Ashraf Matrawy FedML 28 18 0 08 Jan 2021
Advances in Electron Microscopy with Deep Learning Jeffrey M. Ede 35 2 0 04 Jan 2021
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses Micah Goldblum Dimitris Tsipras Chulin Xie Xinyun Chen Avi Schwarzschild D. Song A. Madry Bo-wen Li Tom Goldstein SILM 27 270 0 18 Dec 2020
Robustness Threats of Differential Privacy Nurislam Tursynbek Aleksandr Petiushko Ivan Oseledets AAML 27 14 0 14 Dec 2020
DSRNA: Differentiable Search of Robust Neural Architectures Ramtin Hosseini Xingyi Yang P. Xie OOD AAML 29 50 0 11 Dec 2020
Data-Dependent Randomized Smoothing Motasem Alfarra Adel Bibi Philip Torr Guohao Li UQCV 28 34 0 08 Dec 2020
How Robust are Randomized Smoothing based Defenses to Data Poisoning? Akshay Mehra B. Kailkhura Pin-Yu Chen Jihun Hamm OOD AAML 20 32 0 02 Dec 2020
Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations Jinyuan Jia Binghui Wang Xiaoyu Cao Hongbin Liu Neil Zhenqiang Gong 16 24 0 15 Nov 2020
Reliable Graph Neural Networks via Robust Aggregation Simon Geisler Daniel Zügner Stephan Günnemann AAML OOD 6 71 0 29 Oct 2020
Mitigating Sybil Attacks on Differential Privacy based Federated Learning Yupeng Jiang Yong Li Yipeng Zhou Xi Zheng FedML AAML 29 15 0 20 Oct 2020
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 234 680 0 19 Oct 2020
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness Guillermo Ortiz-Jiménez Apostolos Modas Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 29 48 0 19 Oct 2020
A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning Hongjun Wang Guanbin Li Xiaobai Liu Liang Lin GAN AAML 21 22 0 15 Oct 2020
Higher-Order Certification for Randomized Smoothing Jeet Mohapatra Ching-Yun Ko Tsui-Wei Weng Pin-Yu Chen Sijia Liu Luca Daniel AAML 20 44 0 13 Oct 2020
Adversarial Boot Camp: label free certified robustness in one epoch Ryan Campbell Chris Finlay Adam M. Oberman AAML 25 0 0 05 Oct 2020
Query complexity of adversarial attacks Grzegorz Gluch R. Urbanke AAML 24 5 0 02 Oct 2020
Optimal Provable Robustness of Quantum Classification via Quantum Hypothesis Testing Maurice Weber Nana Liu Bo-wen Li Ce Zhang Zhikuan Zhao AAML 37 28 0 21 Sep 2020
Adversarial Training with Stochastic Weight Average Joong-won Hwang Youngwan Lee Sungchan Oh Yuseok Bae OOD AAML 29 11 0 21 Sep 2020
Review: Deep Learning in Electron Microscopy Jeffrey M. Ede 36 79 0 17 Sep 2020
Certifying Confidence via Randomized Smoothing Aounon Kumar Alexander Levine S. Feizi Tom Goldstein UQCV 33 39 0 17 Sep 2020
SoK: Certified Robustness for Deep Neural Networks Linyi Li Tao Xie Bo-wen Li AAML 33 128 0 09 Sep 2020
Robust Machine Learning via Privacy/Rate-Distortion Theory Ye Wang Shuchin Aeron Adnan Siraj Rakin T. Koike-Akino P. Moulin OOD 22 6 0 22 Jul 2020