Adversarial Examples Are a Natural Consequence of Test Error in Noise

29 January 2019

Justin Gilmer

Papers citing "Adversarial Examples Are a Natural Consequence of Test Error in Noise"

46 / 196 papers shown

Title
Adversarial Distributional Training for Robust Deep Learning Yinpeng Dong Zhijie Deng Tianyu Pang Hang Su Jun Zhu OOD 6 121 0 14 Feb 2020
Adversarial Data Encryption Yingdong Hu Liang Zhang W. Shan Xiaoxiao Qin Jinghuai Qi Zhenzhou Wu Yang Yuan FedML MedIm 15 0 0 10 Feb 2020
On the Robustness of Face Recognition Algorithms Against Attacks and Bias Richa Singh Akshay Agarwal Maneet Singh Shruti Nagpal Mayank Vatsa CVBM AAML 44 65 0 07 Feb 2020
Understanding the Decision Boundary of Deep Neural Networks: An Empirical Study David Mickisch F. Assion Florens Greßner W. Günther M. Motta AAML 11 34 0 05 Feb 2020
REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild Rahul Duggal Scott Freitas Cao Xiao Duen Horng Chau Jimeng Sun 20 22 0 29 Jan 2020
A simple way to make neural networks robust against diverse image corruptions E. Rusak Lukas Schott Roland S. Zimmermann Julian Bitterwolf Oliver Bringmann Matthias Bethge Wieland Brendel 19 64 0 16 Jan 2020
Reject Illegal Inputs with Generative Classifier Derived from Any Discriminative Classifier Xin Wang 11 0 0 02 Jan 2020
Jacobian Adversarially Regularized Networks for Robustness Alvin Chan Yi Tay Yew-Soon Ong Jie Fu AAML 10 74 0 21 Dec 2019
Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation Tianhong Dai Kai Arulkumaran Tamara Gerbert Samyakh Tukra Feryal M. P. Behbahani Anil Anthony Bharath 9 27 0 18 Dec 2019
Statistically Robust Neural Network Classification Benjie Wang Stefan Webb Tom Rainforth OOD AAML 8 19 0 10 Dec 2019
CAMUS: A Framework to Build Formal Specifications for Deep Perception Systems Using Simulators Julien Girard-Satabin Guillaume Charpiat Zakaria Chihani Marc Schoenauer OOD AAML 9 2 0 25 Nov 2019
Robust Deep Neural Networks Inspired by Fuzzy Logic Minh Le OOD AAML AI4CE 17 0 0 20 Nov 2019
Defective Convolutional Networks Tiange Luo Tianle Cai Mengxiao Zhang Siyu Chen Di He Liwei Wang AAML 14 3 0 19 Nov 2019
ODE guided Neural Data Augmentation Techniques for Time Series Data and its Benefits on Robustness A. Sarkar A. Raj Raghu Sesha Iyengar AAML AI4TS 26 0 0 15 Oct 2019
Predicting with High Correlation Features Devansh Arpit Caiming Xiong R. Socher OODD OOD 12 7 0 01 Oct 2019
RandAugment: Practical automated data augmentation with a reduced search space E. D. Cubuk Barret Zoph Jonathon Shlens Quoc V. Le MQ 34 3,412 0 30 Sep 2019
Lower Bounds on Adversarial Robustness from Optimal Transport A. Bhagoji Daniel Cullina Prateek Mittal OOD OT AAML 17 92 0 26 Sep 2019
Towards Model-Agnostic Adversarial Defenses using Adversarially Trained Autoencoders Pratik Vaishnavi Kevin Eykholt A. Prakash Amir Rahmati AAML 12 2 0 12 Sep 2019
Structural Robustness for Deep Learning Architectures Carlos Lassance Vincent Gripon Jian Tang Antonio Ortega OOD 14 2 0 11 Sep 2019
PDA: Progressive Data Augmentation for General Robustness of Deep Neural Networks Hang Yu Aishan Liu Xianglong Liu Gen Li Ping Luo R. Cheng Jichen Yang Chongzhi Zhang AAML 16 10 0 11 Sep 2019
Benchmarking the Robustness of Semantic Segmentation Models Christoph Kamann Carsten Rother VLM UQCV 6 160 0 14 Aug 2019
Robustness properties of Facebook's ResNeXt WSL models Emin Orhan VLM 8 30 0 17 Jul 2019
Stateful Detection of Black-Box Adversarial Attacks Steven Chen Nicholas Carlini D. Wagner AAML MLAU 12 119 0 12 Jul 2019
Learning Data Augmentation Strategies for Object Detection Barret Zoph E. D. Cubuk Golnaz Ghiasi Tsung-Yi Lin Jonathon Shlens Quoc V. Le 25 523 0 26 Jun 2019
Quantitative Verification of Neural Networks And its Security Applications Teodora Baluta Shiqi Shen Shweta Shinde Kuldeep S. Meel P. Saxena AAML 11 104 0 25 Jun 2019
A Fourier Perspective on Model Robustness in Computer Vision Dong Yin Raphael Gontijo-Lopes Jonathon Shlens E. D. Cubuk Justin Gilmer OOD 15 486 0 21 Jun 2019
Lower Bounds for Adversarially Robust PAC Learning Dimitrios I. Diochnos Saeed Mahloujifar Mohammad Mahmoody AAML 11 26 0 13 Jun 2019
Using learned optimizers to make models robust to input noise Luke Metz Niru Maheswaranathan Jonathon Shlens Jascha Narain Sohl-Dickstein E. D. Cubuk VLM OOD 13 26 0 08 Jun 2019
A cryptographic approach to black box adversarial machine learning Kevin Shi Daniel J. Hsu Allison Bishop AAML 6 3 0 07 Jun 2019
Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift Xudong Sun Alexej Gossmann Yu Wang B. Bischl OOD 17 5 0 07 Jun 2019
Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation Raphael Gontijo-Lopes Dong Yin Ben Poole Justin Gilmer E. D. Cubuk AAML 16 204 0 06 Jun 2019
MNIST-C: A Robustness Benchmark for Computer Vision Norman Mu Justin Gilmer 8 203 0 05 Jun 2019
Multi-way Encoding for Robustness Donghyun Kim Sarah Adel Bargal Jianming Zhang Stan Sclaroff AAML 8 2 0 05 Jun 2019
Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward A. Qayyum Muhammad Usama Junaid Qadir Ala I. Al-Fuqaha AAML 19 187 0 29 May 2019
Convergence and Margin of Adversarial Training on Separable Data Zachary B. Charles Shashank Rajput S. Wright Dimitris Papailiopoulos AAML 18 16 0 22 May 2019
Does Data Augmentation Lead to Positive Margin? Shashank Rajput Zhili Feng Zachary B. Charles Po-Ling Loh Dimitris Papailiopoulos 17 37 0 08 May 2019
Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran A. Madry SILM 11 1,806 0 06 May 2019
Batch Normalization is a Cause of Adversarial Vulnerability A. Galloway A. Golubeva T. Tanay M. Moussa Graham W. Taylor ODL AAML 9 80 0 06 May 2019
Rallying Adversarial Techniques against Deep Learning for Network Security Joseph Clements Yuzhe Yang Ankur A Sharma Hongxin Hu Yingjie Lao AAML 15 51 0 27 Mar 2019
Towards Understanding Adversarial Examples Systematically: Exploring Data Size, Task and Model Factors Ke Sun Zhanxing Zhu Zhouchen Lin AAML 11 18 0 28 Feb 2019
On Evaluating Adversarial Robustness Nicholas Carlini Anish Athalye Nicolas Papernot Wieland Brendel Jonas Rauber Dimitris Tsipras Ian Goodfellow A. Madry Alexey Kurakin ELM AAML 6 890 0 18 Feb 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 17 1,990 0 08 Feb 2019
Random Spiking and Systematic Evaluation of Defenses Against Adversarial Examples Huangyi Ge Sze Yiu Chau Bruno Ribeiro Ninghui Li AAML 16 1 0 05 Dec 2018
Certified Adversarial Robustness with Additive Noise Bai Li Changyou Chen Wenlin Wang Lawrence Carin AAML 15 341 0 10 Sep 2018
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression Nilaksh Das Madhuri Shanbhogue Shang-Tse Chen Fred Hohman Siwei Li Li-Wei Chen Michael E. Kounavis Duen Horng Chau FedML AAML 38 224 0 19 Feb 2018
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 226 1,835 0 03 Feb 2017