Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong

15 June 2017

Papers citing "Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong"

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Title
Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning Michael Everett Bjorn Lutjens Jonathan P. How AAML 13 41 0 11 Apr 2020
Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes Sravanti Addepalli S. VivekB. Arya Baburaj Gaurang Sriramanan R. Venkatesh Babu AAML 6 32 0 01 Apr 2020
Denoised Smoothing: A Provable Defense for Pretrained Classifiers Hadi Salman Mingjie Sun Greg Yang Ashish Kapoor J. Zico Kolter 37 23 0 04 Mar 2020
Revisiting Ensembles in an Adversarial Context: Improving Natural Accuracy Aditya Saligrama Guillaume Leclerc AAML 11 1 0 26 Feb 2020
Adversarial Ranking Attack and Defense Mo Zhou Zhenxing Niu Le Wang Qilin Zhang G. Hua 36 38 0 26 Feb 2020
Towards Backdoor Attacks and Defense in Robust Machine Learning Models E. Soremekun Sakshi Udeshi Sudipta Chattopadhyay AAML 22 13 0 25 Feb 2020
Robustness from Simple Classifiers Sharon Qian Dimitris Kalimeris Gal Kaplun Yaron Singer AAML 8 1 0 21 Feb 2020
More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Models Lin Chen Yifei Min Mingrui Zhang Amin Karbasi OOD 38 64 0 11 Feb 2020
An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models Yao Deng Xi Zheng Tianyi Zhang Chen Chen Guannan Lou Miryung Kim AAML 8 141 0 06 Feb 2020
Secure and Robust Machine Learning for Healthcare: A Survey A. Qayyum Junaid Qadir Muhammad Bilal Ala I. Al-Fuqaha AAML OOD 45 375 0 21 Jan 2020
ATHENA: A Framework based on Diverse Weak Defenses for Building Adversarial Defense Meng Jianhai Su Jason M. O'Kane Pooyan Jamshidi AAML 9 7 0 02 Jan 2020
Exploiting the Sensitivity of $L_2$ Adversarial Examples to Erase-and-Restore F. Zuo Qiang Zeng AAML 10 1 0 01 Jan 2020
Exploring the Back Alleys: Analysing The Robustness of Alternative Neural Network Architectures against Adversarial Attacks Y. Tan Yuval Elovici Alexander Binder AAML 11 3 0 08 Dec 2019
Towards Large yet Imperceptible Adversarial Image Perturbations with Perceptual Color Distance Zhengyu Zhao Zhuoran Liu Martha Larson AAML 18 142 0 06 Nov 2019
Enhancing Certifiable Robustness via a Deep Model Ensemble Huan Zhang Minhao Cheng Cho-Jui Hsieh 33 9 0 31 Oct 2019
Certified Adversarial Robustness for Deep Reinforcement Learning Björn Lütjens Michael Everett Jonathan P. How AAML 6 91 0 28 Oct 2019
A Useful Taxonomy for Adversarial Robustness of Neural Networks L. Smith AAML 28 6 0 23 Oct 2019
Defending Against Physically Realizable Attacks on Image Classification Tong Wu Liang Tong Yevgeniy Vorobeychik AAML 19 125 0 20 Sep 2019
Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems Xingjun Ma Yuhao Niu Lin Gu Yisen Wang Yitian Zhao James Bailey Feng Lu MedIm AAML 22 444 0 24 Jul 2019
Towards Stable and Efficient Training of Verifiably Robust Neural Networks Huan Zhang Hongge Chen Chaowei Xiao Sven Gowal Robert Stanforth Bo-wen Li Duane S. Boning Cho-Jui Hsieh AAML 17 344 0 14 Jun 2019
Robust Attacks against Multiple Classifiers Juan C. Perdomo Yaron Singer AAML 13 10 0 06 Jun 2019
Enhancing Gradient-based Attacks with Symbolic Intervals Shiqi Wang Yizheng Chen Ahmed Abdou Suman Jana AAML 28 15 0 05 Jun 2019
Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss Pengcheng Li Jinfeng Yi Bowen Zhou Lijun Zhang AAML 29 36 0 28 May 2019
Regula Sub-rosa: Latent Backdoor Attacks on Deep Neural Networks Yuanshun Yao Huiying Li Haitao Zheng Ben Y. Zhao AAML 27 13 0 24 May 2019
Testing DNN Image Classifiers for Confusion & Bias Errors Yuchi Tian Ziyuan Zhong Vicente Ordonez Gail E. Kaiser Baishakhi Ray 24 52 0 20 May 2019
Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples Vikash Sehwag A. Bhagoji Liwei Song Chawin Sitawarin Daniel Cullina M. Chiang Prateek Mittal OODD 24 26 0 05 May 2019
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks Yandong Li Lijun Li Liqiang Wang Tong Zhang Boqing Gong AAML 18 245 0 01 May 2019
Gotta Catch Ém All: Using Honeypots to Catch Adversarial Attacks on Neural Networks Shawn Shan Emily Wenger Bolun Wang Yangqiu Song Haitao Zheng Ben Y. Zhao 23 71 0 18 Apr 2019
Adversarial Reinforcement Learning under Partial Observability in Autonomous Computer Network Defence Yi Han David Hubczenko Paul Montague O. Vel Tamas Abraham Benjamin I. P. Rubinstein C. Leckie T. Alpcan S. Erfani AAML 11 6 0 25 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 20 890 0 18 Feb 2019
AuxBlocks: Defense Adversarial Example via Auxiliary Blocks Yueyao Yu Pengfei Yu Wenye Li AAML 6 6 0 18 Feb 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
Theoretically Principled Trade-off between Robustness and Accuracy Hongyang R. Zhang Yaodong Yu Jiantao Jiao Eric Xing L. Ghaoui Michael I. Jordan 36 2,496 0 24 Jan 2019
Noise Flooding for Detecting Audio Adversarial Examples Against Automatic Speech Recognition K. Rajaratnam Jugal Kalita AAML 14 44 0 25 Dec 2018
Exploiting the Inherent Limitation of L0 Adversarial Examples F. Zuo Bokai Yang Xiaopeng Li Lannan Luo Qiang Zeng AAML 21 1 0 23 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
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
Security Matters: A Survey on Adversarial Machine Learning Guofu Li Pengjia Zhu Jin Li Zhemin Yang Ning Cao Zhiyi Chen AAML 26 24 0 16 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
Secure Deep Learning Engineering: A Software Quality Assurance Perspective Lei Ma Felix Juefei Xu Minhui Xue Q. Hu Sen Chen Bo-wen Li Yang Liu Jianjun Zhao Jianxiong Yin Simon See AAML 27 35 0 10 Oct 2018
Is PGD-Adversarial Training Necessary? Alternative Training via a Soft-Quantization Network with Noisy-Natural Samples Only T. Zheng Changyou Chen K. Ren AAML 20 6 0 10 Oct 2018
WAIC, but Why? Generative Ensembles for Robust Anomaly Detection Hyun-Jae Choi Eric Jang Alexander A. Alemi OODD 20 82 0 02 Oct 2018
Adversarial Examples - A Complete Characterisation of the Phenomenon A. Serban E. Poll Joost Visser SILM AAML 30 49 0 02 Oct 2018
Characterizing Audio Adversarial Examples Using Temporal Dependency Zhuolin Yang Bo-wen Li Pin-Yu Chen D. Song AAML 6 161 0 28 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
Reinforcement Learning for Autonomous Defence in Software-Defined Networking Yi Han Benjamin I. P. Rubinstein Tamas Abraham T. Alpcan O. Vel S. Erfani David Hubczenko C. Leckie Paul Montague AAML 16 68 0 17 Aug 2018
Security and Privacy Issues in Deep Learning Ho Bae Jaehee Jang Dahuin Jung Hyemi Jang Heonseok Ha Hyungyu Lee Sungroh Yoon SILM MIACV 45 77 0 31 Jul 2018
Evaluating and Understanding the Robustness of Adversarial Logit Pairing Logan Engstrom Andrew Ilyas Anish Athalye AAML 14 140 0 26 Jul 2018
Gradient Similarity: An Explainable Approach to Detect Adversarial Attacks against Deep Learning J. Dhaliwal S. Shintre AAML 23 15 0 27 Jun 2018