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 / 352 papers shown

Title
BlurNet: Defense by Filtering the Feature Maps Ravi Raju Mikko H. Lipasti AAML 39 15 0 06 Aug 2019
Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation Utku Ozbulak Arnout Van Messem W. D. Neve MedIm AAML 20 60 0 30 Jul 2019
Natural Adversarial Examples Dan Hendrycks Kevin Zhao Steven Basart Jacob Steinhardt D. Song OODD 86 1,422 0 16 Jul 2019
Metamorphic Detection of Adversarial Examples in Deep Learning Models With Affine Transformations R. Mekala Gudjon Magnusson Adam A. Porter Mikael Lindvall Madeline Diep AAML 6 16 0 10 Jul 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
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack Francesco Croce Matthias Hein AAML 43 474 0 03 Jul 2019
Adversarial Examples to Fool Iris Recognition Systems Sobhan Soleymani Ali Dabouei J. Dawson Nasser M. Nasrabadi GAN AAML 16 16 0 21 Jun 2019
Machine Learning Testing: Survey, Landscapes and Horizons Jie M. Zhang Mark Harman Lei Ma Yang Liu VLM AILaw 39 739 0 19 Jun 2019
Robust or Private? Adversarial Training Makes Models More Vulnerable to Privacy Attacks Felipe A. Mejia Paul Gamble Z. Hampel-Arias M. Lomnitz Nina Lopatina Lucas Tindall M. Barrios SILM 27 18 0 15 Jun 2019
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks R. Sahay Rehana Mahfuz Aly El Gamal AAML 22 5 0 13 Jun 2019
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers Hadi Salman Greg Yang Jungshian Li Pengchuan Zhang Huan Zhang Ilya P. Razenshteyn Sébastien Bubeck AAML 33 536 0 09 Jun 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
Enhancing Gradient-based Attacks with Symbolic Intervals Shiqi Wang Yizheng Chen Ahmed Abdou Suman Jana AAML 28 15 0 05 Jun 2019
Bypassing Backdoor Detection Algorithms in Deep Learning T. Tan Reza Shokri FedML AAML 39 149 0 31 May 2019
Scaleable input gradient regularization for adversarial robustness Chris Finlay Adam M. Oberman AAML 16 77 0 27 May 2019
Taking Care of The Discretization Problem: A Comprehensive Study of the Discretization Problem and A Black-Box Adversarial Attack in Discrete Integer Domain Lei Bu Yuchao Duan Fu Song Zhe Zhao AAML 32 18 0 19 May 2019
What Do Adversarially Robust Models Look At? Takahiro Itazuri Yoshihiro Fukuhara Hirokatsu Kataoka Shigeo Morishima 19 5 0 19 May 2019
AI Enabling Technologies: A Survey V. Gadepally Justin A. Goodwin J. Kepner Albert Reuther Hayley Reynolds S. Samsi Jonathan Su David Martinez 27 24 0 08 May 2019
Test Selection for Deep Learning Systems Wei Ma Mike Papadakis Anestis Tsakmalis Maxime Cordy Yves Le Traon OOD 21 92 0 30 Apr 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
A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning Shahbaz Rezaei Xin Liu SILM AAML 33 46 0 08 Apr 2019
Interpreting Adversarial Examples by Activation Promotion and Suppression Kaidi Xu Sijia Liu Gaoyuan Zhang Mengshu Sun Pu Zhao Quanfu Fan Chuang Gan X. Lin AAML FAtt 24 43 0 03 Apr 2019
Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks Francesco Croce Jonas Rauber Matthias Hein AAML 20 30 0 27 Mar 2019
Detecting Overfitting via Adversarial Examples Roman Werpachowski András Gyorgy Csaba Szepesvári TDI 26 45 0 06 Mar 2019
Attacking Graph-based Classification via Manipulating the Graph Structure Binghui Wang Neil Zhenqiang Gong AAML 28 152 0 01 Mar 2019
Quantifying Perceptual Distortion of Adversarial Examples Matt Jordan N. Manoj Surbhi Goel A. Dimakis 19 39 0 21 Feb 2019
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples Kevin Roth Yannic Kilcher Thomas Hofmann AAML 27 175 0 13 Feb 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 17 1,992 0 08 Feb 2019
A New Family of Neural Networks Provably Resistant to Adversarial Attacks Rakshit Agrawal Luca de Alfaro D. Helmbold AAML OOD 27 2 0 01 Feb 2019
Adversarial Examples Are a Natural Consequence of Test Error in Noise Nic Ford Justin Gilmer Nicholas Carlini E. D. Cubuk AAML 27 318 0 29 Jan 2019
Using Pre-Training Can Improve Model Robustness and Uncertainty Dan Hendrycks Kimin Lee Mantas Mazeika NoLa 34 719 0 28 Jan 2019
Improving Adversarial Robustness via Promoting Ensemble Diversity Tianyu Pang Kun Xu Chao Du Ning Chen Jun Zhu AAML 41 434 0 25 Jan 2019
Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers Daniel Liu Ronald Yu Hao Su 3DPC 34 165 0 10 Jan 2019
Adversarial Examples Versus Cloud-based Detectors: A Black-box Empirical Study Xurong Li S. Ji Men Han Juntao Ji Zhenyu Ren Yushan Liu Chunming Wu AAML 21 31 0 04 Jan 2019
A Multiversion Programming Inspired Approach to Detecting Audio Adversarial Examples Qiang Zeng Jianhai Su Chenglong Fu Golam Kayas Lannan Luo AAML 24 46 0 26 Dec 2018
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem Matthias Hein Maksym Andriushchenko Julian Bitterwolf OODD 46 552 0 13 Dec 2018
Learning Transferable Adversarial Examples via Ghost Networks Yingwei Li S. Bai Yuyin Zhou Cihang Xie Zhishuai Zhang Alan Yuille AAML 39 136 0 09 Dec 2018
Random Spiking and Systematic Evaluation of Defenses Against Adversarial Examples Huangyi Ge Sze Yiu Chau Bruno Ribeiro Ninghui Li AAML 27 1 0 05 Dec 2018
Interpretable Deep Learning under Fire Xinyang Zhang Ningfei Wang Hua Shen S. Ji Xiapu Luo Ting Wang AAML AI4CE 22 169 0 03 Dec 2018
SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems Edward Chou Florian Tramèr Giancarlo Pellegrino AAML 170 287 0 02 Dec 2018
A randomized gradient-free attack on ReLU networks Francesco Croce Matthias Hein AAML 37 21 0 28 Nov 2018
AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning K. Makarychev Pascal Dupré Yury Makarychev Giancarlo Pellegrino Dan Boneh AAML 29 64 0 08 Nov 2018
MixTrain: Scalable Training of Verifiably Robust Neural Networks Yue Zhang Yizheng Chen Ahmed Abdou Mohsen Guizani AAML 21 23 0 06 Nov 2018
On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm Tsui-Wei Weng Huan Zhang Pin-Yu Chen A. Lozano Cho-Jui Hsieh Luca Daniel 28 10 0 19 Oct 2018
Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation Chaowei Xiao Ruizhi Deng Bo-wen Li Feng Yu M. Liu D. Song AAML 16 99 0 11 Oct 2018
What made you do this? Understanding black-box decisions with sufficient input subsets Brandon Carter Jonas W. Mueller Siddhartha Jain David K Gifford FAtt 37 77 0 09 Oct 2018
Detecting DGA domains with recurrent neural networks and side information Ryan R. Curtin Andrew B. Gardner Slawomir Grzonkowski A. Kleymenov Alejandro Mosquera AAML 6 64 0 04 Oct 2018
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network Xuanqing Liu Yao Li Chongruo Wu Cho-Jui Hsieh AAML OOD 24 171 0 01 Oct 2018
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks Kenneth T. Co Luis Muñoz-González Sixte de Maupeou Emil C. Lupu AAML 22 67 0 30 Sep 2018
On The Utility of Conditional Generation Based Mutual Information for Characterizing Adversarial Subspaces Chia-Yi Hsu Pei-Hsuan Lu Pin-Yu Chen Chia-Mu Yu AAML 30 1 0 24 Sep 2018