Early Methods for Detecting Adversarial Images

1 August 2016

Papers citing "Early Methods for Detecting Adversarial Images"

50 / 101 papers shown

Title
DropCluster: A structured dropout for convolutional networks Liyang Chen P. Gautier Sergul Aydore 25 11 0 07 Feb 2020
RAID: Randomized Adversarial-Input Detection for Neural Networks Hasan Ferit Eniser M. Christakis Valentin Wüstholz AAML 25 15 0 07 Feb 2020
GhostImage: Remote Perception Attacks against Camera-based Image Classification Systems Yanmao Man Ming Li Ryan M. Gerdes AAML 22 8 0 21 Jan 2020
DLA: Dense-Layer-Analysis for Adversarial Example Detection Philip Sperl Ching-yu Kao Peng Chen Konstantin Böttinger AAML 11 34 0 05 Nov 2019
Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks David Stutz Matthias Hein Bernt Schiele AAML 25 5 0 14 Oct 2019
Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction Method Artur Jordão M. Lie V. H. C. Melo William Robson Schwartz 23 3 0 05 Oct 2019
Towards neural networks that provably know when they don't know Alexander Meinke Matthias Hein OODD 33 139 0 26 Sep 2019
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review Han Xu Yao Ma Haochen Liu Debayan Deb Hui Liu Jiliang Tang Anil K. Jain AAML 33 669 0 17 Sep 2019
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions Yao Qin Nicholas Frosst S. Sabour Colin Raffel G. Cottrell Geoffrey E. Hinton GAN AAML 19 71 0 05 Jul 2019
ML-LOO: Detecting Adversarial Examples with Feature Attribution Puyudi Yang Jianbo Chen Cho-Jui Hsieh Jane-ling Wang Michael I. Jordan AAML 22 101 0 08 Jun 2019
GAT: Generative Adversarial Training for Adversarial Example Detection and Robust Classification Xuwang Yin Soheil Kolouri Gustavo K. Rohde AAML 30 43 0 27 May 2019
Moving Target Defense for Deep Visual Sensing against Adversarial Examples Qun Song Zhenyu Yan Rui Tan AAML 21 20 0 11 May 2019
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks David J. Miller Zhen Xiang G. Kesidis AAML 19 35 0 12 Apr 2019
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations Dan Hendrycks Thomas G. Dietterich OOD VLM 12 3,358 0 28 Mar 2019
A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations Saeid Asgari Taghanaki Kumar Abhishek Shekoofeh Azizi Ghassan Hamarneh AAML 31 40 0 03 Mar 2019
Natural and Adversarial Error Detection using Invariance to Image Transformations Yuval Bahat Michal Irani Gregory Shakhnarovich AAML 9 18 0 01 Feb 2019
Using Pre-Training Can Improve Model Robustness and Uncertainty Dan Hendrycks Kimin Lee Mantas Mazeika NoLa 34 721 0 28 Jan 2019
A Black-box Attack on Neural Networks Based on Swarm Evolutionary Algorithm Xiaolei Liu Yuheng Luo Xiaosong Zhang Qingxin Zhu AAML 24 16 0 26 Jan 2019
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability Xiaowei Huang Daniel Kroening Wenjie Ruan Marta Kwiatkowska Youcheng Sun Emese Thamo Min Wu Xinping Yi AAML 24 50 0 18 Dec 2018
Spartan Networks: Self-Feature-Squeezing Neural Networks for increased robustness in adversarial settings François Menet Paul Berthier José M. Fernandez M. Gagnon AAML 15 10 0 17 Dec 2018
SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems Edward Chou Florian Tramèr Giancarlo Pellegrino AAML 182 289 0 02 Dec 2018
Detecting Adversarial Perturbations Through Spatial Behavior in Activation Spaces Ziv Katzir Yuval Elovici AAML 11 26 0 22 Nov 2018
Adversarial Examples - A Complete Characterisation of the Phenomenon A. Serban E. Poll Joost Visser SILM AAML 33 49 0 02 Oct 2018
Adversarial Examples: Opportunities and Challenges Jiliang Zhang Chen Li AAML 17 233 0 13 Sep 2018
Bridging machine learning and cryptography in defence against adversarial attacks O. Taran Shideh Rezaeifar Slava Voloshynovskiy AAML 15 22 0 05 Sep 2018
Mitigation of Adversarial Attacks through Embedded Feature Selection Ziyi Bao Luis Muñoz-González Emil C. Lupu AAML 17 1 0 16 Aug 2018
Simultaneous Adversarial Training - Learn from Others Mistakes Zukang Liao AAML GAN 22 4 0 21 Jul 2018
Motivating the Rules of the Game for Adversarial Example Research Justin Gilmer Ryan P. Adams Ian Goodfellow David G. Andersen George E. Dahl AAML 50 226 0 18 Jul 2018
Implicit Generative Modeling of Random Noise during Training for Adversarial Robustness Priyadarshini Panda Kaushik Roy AAML 22 4 0 05 Jul 2018
Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations Dan Hendrycks Thomas G. Dietterich OOD 22 197 0 04 Jul 2018
Gradient Similarity: An Explainable Approach to Detect Adversarial Attacks against Deep Learning J. Dhaliwal S. Shintre AAML 31 15 0 27 Jun 2018
Detection based Defense against Adversarial Examples from the Steganalysis Point of View Jiayang Liu Weiming Zhang Yiwei Zhang Dongdong Hou Yujia Liu Hongyue Zha Nenghai Yu AAML 25 99 0 21 Jun 2018
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models Pouya Samangouei Maya Kabkab Rama Chellappa AAML GAN 29 1,168 0 17 May 2018
Breaking Transferability of Adversarial Samples with Randomness Yan Zhou Murat Kantarcioglu B. Xi AAML 27 12 0 11 May 2018
Defending against Adversarial Images using Basis Functions Transformations Uri Shaham J. Garritano Yutaro Yamada Ethan Weinberger A. Cloninger Xiuyuan Cheng Kelly P. Stanton Y. Kluger AAML 24 57 0 28 Mar 2018
Clipping free attacks against artificial neural networks B. Addad Jérôme Kodjabachian Christophe Meyer AAML 11 1 0 26 Mar 2018
Detecting Adversarial Perturbations with Saliency Chiliang Zhang Zhimou Yang Zuochang Ye AAML 6 32 0 23 Mar 2018
Attack Strength vs. Detectability Dilemma in Adversarial Machine Learning Christopher Frederickson Michael Moore Glenn Dawson R. Polikar AAML 16 32 0 20 Feb 2018
Divide, Denoise, and Defend against Adversarial Attacks Seyed-Mohsen Moosavi-Dezfooli A. Shrivastava Oncel Tuzel AAML 32 45 0 19 Feb 2018
Certified Robustness to Adversarial Examples with Differential Privacy Mathias Lécuyer Vaggelis Atlidakis Roxana Geambasu Daniel J. Hsu Suman Jana SILM AAML 56 926 0 09 Feb 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 98 3,160 0 01 Feb 2018
Adversarial Examples: Attacks and Defenses for Deep Learning Xiaoyong Yuan Pan He Qile Zhu Xiaolin Li SILM AAML 36 1,612 0 19 Dec 2017
When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time David J. Miller Yujia Wang G. Kesidis AAML 18 43 0 18 Dec 2017
Provably Minimally-Distorted Adversarial Examples Nicholas Carlini Guy Katz Clark W. Barrett D. Dill AAML 33 89 0 29 Sep 2017
Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification Xiaoyu Cao Neil Zhenqiang Gong AAML 17 208 0 17 Sep 2017
Learning Universal Adversarial Perturbations with Generative Models Jamie Hayes G. Danezis AAML 15 54 0 17 Aug 2017
Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong Warren He James Wei Xinyun Chen Nicholas Carlini D. Song AAML 43 242 0 15 Jun 2017
Detecting Adversarial Image Examples in Deep Networks with Adaptive Noise Reduction Bin Liang Hongcheng Li Miaoqiang Su Xirong Li Wenchang Shi Xiaofeng Wang AAML 14 216 0 23 May 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 61 1,842 0 20 May 2017
Enhancing Robustness of Machine Learning Systems via Data Transformations A. Bhagoji Daniel Cullina Chawin Sitawarin Prateek Mittal AAML 17 231 0 09 Apr 2017