Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods

20 May 2017

Papers citing "Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods"

50 / 349 papers shown

Title
Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks Weiran Lin Keane Lucas Lujo Bauer Michael K. Reiter Mahmood Sharif AAML 31 5 0 28 Dec 2021
Robust Graph Neural Networks via Probabilistic Lipschitz Constraints R. Arghal Eric Lei Shirin Saeedi Bidokhti 19 19 0 14 Dec 2021
Medical Aegis: Robust adversarial protectors for medical images Qingsong Yao Zecheng He S. Kevin Zhou AAML MedIm 27 2 0 22 Nov 2021
TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems Bao Gia Doan Minhui Xue Shiqing Ma Ehsan Abbasnejad Damith C. Ranasinghe AAML 41 53 0 19 Nov 2021
Data Augmentation Can Improve Robustness Sylvestre-Alvise Rebuffi Sven Gowal D. A. Calian Florian Stimberg Olivia Wiles Timothy A. Mann AAML 17 270 0 09 Nov 2021
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification Maximilian Stadler Bertrand Charpentier Simon Geisler Daniel Zügner Stephan Günnemann UQCV BDL 41 81 0 26 Oct 2021
Can't Fool Me: Adversarially Robust Transformer for Video Understanding D. Choudhary Palash Goyal Saurabh Sahu ViT 36 0 0 26 Oct 2021
Improving Robustness using Generated Data Sven Gowal Sylvestre-Alvise Rebuffi Olivia Wiles Florian Stimberg D. A. Calian Timothy A. Mann 36 293 0 18 Oct 2021
Out-of-Distribution Robustness in Deep Learning Compression Eric Lei Hamed Hassani Shirin Saeedi Bidokhti OOD OODD 8 5 0 13 Oct 2021
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
Mitigating Black-Box Adversarial Attacks via Output Noise Perturbation Manjushree B. Aithal Xiaohua Li AAML 60 6 0 30 Sep 2021
Adversarial Transfer Attacks With Unknown Data and Class Overlap Luke E. Richards A. Nguyen Ryan Capps Steven D. Forsythe Cynthia Matuszek Edward Raff AAML 41 7 0 23 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
Simple Post-Training Robustness Using Test Time Augmentations and Random Forest Gilad Cohen Raja Giryes AAML 35 4 0 16 Sep 2021
SEC4SR: A Security Analysis Platform for Speaker Recognition Guangke Chen Zhe Zhao Fu Song Sen Chen Lingling Fan Yang Liu AAML 25 12 0 04 Sep 2021
Recent advances for quantum classifiers Weikang Li D. Deng AAML 39 84 0 30 Aug 2021
Kryptonite: An Adversarial Attack Using Regional Focus Yogesh Kulkarni Krisha Bhambani AAML 19 3 0 23 Aug 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
AdvDrop: Adversarial Attack to DNNs by Dropping Information Ranjie Duan YueFeng Chen Dantong Niu Yun Yang •. A. K. Qin Yuan He AAML 24 90 0 20 Aug 2021
A Survey on Open Set Recognition Atefeh Mahdavi Marco M. Carvalho BDL 23 35 0 18 Aug 2021
Advances in adversarial attacks and defenses in computer vision: A survey Naveed Akhtar Ajmal Mian Navid Kardan M. Shah AAML 26 235 0 01 Aug 2021
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them Florian Tramèr AAML 30 65 0 24 Jul 2021
Trustworthy AI: A Computational Perspective Haochen Liu Yiqi Wang Wenqi Fan Xiaorui Liu Yaxin Li Shaili Jain Yunhao Liu Anil K. Jain Jiliang Tang FaML 104 196 0 12 Jul 2021
Data Poisoning Won't Save You From Facial Recognition Evani Radiya-Dixit Sanghyun Hong Nicholas Carlini Florian Tramèr AAML PICV 15 57 0 28 Jun 2021
Certified Robustness via Randomized Smoothing over Multiplicative Parameters of Input Transformations Nikita Muravev Aleksandr Petiushko AAML 18 7 0 28 Jun 2021
Adversarial Examples in Multi-Layer Random ReLU Networks Peter L. Bartlett Sébastien Bubeck Yeshwanth Cherapanamjeri AAML GAN 27 28 0 23 Jun 2021
Attack to Fool and Explain Deep Networks Naveed Akhtar M. Jalwana Bennamoun Ajmal Mian AAML 27 33 0 20 Jun 2021
Evaluating the Robustness of Trigger Set-Based Watermarks Embedded in Deep Neural Networks Suyoung Lee Wonho Song Suman Jana M. Cha Sooel Son AAML 11 13 0 18 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
Taxonomy of Machine Learning Safety: A Survey and Primer Sina Mohseni Haotao Wang Zhiding Yu Chaowei Xiao Zhangyang Wang J. Yadawa 21 31 0 09 Jun 2021
Reveal of Vision Transformers Robustness against Adversarial Attacks Ahmed Aldahdooh W. Hamidouche Olivier Déforges ViT 15 56 0 07 Jun 2021
A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks Jacob Mitchell Springer Melanie Mitchell Garrett Kenyon AAML 31 43 0 03 Jun 2021
BAARD: Blocking Adversarial Examples by Testing for Applicability, Reliability and Decidability Luke Chang Katharina Dost Kaiqi Zhao Ambra Demontis Fabio Roli Gillian Dobbie Jörg Simon Wicker AAML 19 2 0 02 May 2021
Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense Haoxi Zhan Xiaobing Pei AAML 24 9 0 30 Apr 2021
Adaptive Adversarial Training for Meta Reinforcement Learning Shiqi Chen Zhengyu Chen Donglin Wang 30 6 0 27 Apr 2021
Relating Adversarially Robust Generalization to Flat Minima David Stutz Matthias Hein Bernt Schiele OOD 32 65 0 09 Apr 2021
Ensemble-in-One: Learning Ensemble within Random Gated Networks for Enhanced Adversarial Robustness Yi Cai Xuefei Ning Huazhong Yang Yu Wang AAML 27 4 0 27 Mar 2021
MagDR: Mask-guided Detection and Reconstruction for Defending Deepfakes Zhikai Chen Lingxi Xie Shanmin Pang Yong He Bo Zhang AAML 36 32 0 26 Mar 2021
Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles G. Cantareira R. Mello F. Paulovich AAML 24 9 0 18 Mar 2021
AI Fairness via Domain Adaptation Neil J. Joshi Philippe Burlina 21 15 0 15 Mar 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
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink Ranjie Duan Xiaofeng Mao •. A. K. Qin Yun Yang YueFeng Chen Shaokai Ye Yuan He AAML 24 138 0 11 Mar 2021
WaveGuard: Understanding and Mitigating Audio Adversarial Examples Shehzeen Samarah Hussain Paarth Neekhara Shlomo Dubnov Julian McAuley F. Koushanfar AAML 30 71 0 04 Mar 2021
SpectralDefense: Detecting Adversarial Attacks on CNNs in the Fourier Domain P. Harder Franz-Josef Pfreundt M. Keuper J. Keuper AAML 27 48 0 04 Mar 2021
Fixing Data Augmentation to Improve Adversarial Robustness Sylvestre-Alvise Rebuffi Sven Gowal D. A. Calian Florian Stimberg Olivia Wiles Timothy A. Mann AAML 36 269 0 02 Mar 2021
Effective and Efficient Vote Attack on Capsule Networks Jindong Gu Baoyuan Wu Volker Tresp AAML 17 26 0 19 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
Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS Felix O. Olowononi D. Rawat Chunmei Liu 34 132 0 14 Feb 2021
Mixed Nash Equilibria in the Adversarial Examples Game Laurent Meunier M. Scetbon Rafael Pinot Jamal Atif Y. Chevaleyre AAML 23 29 0 13 Feb 2021
Dompteur: Taming Audio Adversarial Examples Thorsten Eisenhofer Lea Schonherr Joel Frank Lars Speckemeier D. Kolossa Thorsten Holz AAML 33 24 0 10 Feb 2021