Ensemble Adversarial Training: Attacks and Defenses

19 May 2017

Dan Boneh

Papers citing "Ensemble Adversarial Training: Attacks and Defenses"

50 / 1,344 papers shown

Title
On the Need for Topology-Aware Generative Models for Manifold-Based Defenses Uyeong Jang Susmit Jha S. Jha AAML 33 13 0 07 Sep 2019
Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents Xian Yeow Lee Sambit Ghadai Kai Liang Tan Chinmay Hegde Soumik Sarkar AAML 27 49 0 05 Sep 2019
Are Adversarial Robustness and Common Perturbation Robustness Independent Attributes ? Alfred Laugros A. Caplier Matthieu Ospici AAML 22 40 0 04 Sep 2019
Metric Learning for Adversarial Robustness Chengzhi Mao Ziyuan Zhong Junfeng Yang Carl Vondrick Baishakhi Ray OOD 27 184 0 03 Sep 2019
Universal, transferable and targeted adversarial attacks Junde Wu Rao Fu AAML SILM 31 10 0 29 Aug 2019
Defeating Misclassification Attacks Against Transfer Learning Bang Wu Shuo Wang Xingliang Yuan Cong Wang Carsten Rudolph Xiangwen Yang AAML 24 6 0 29 Aug 2019
Deep Neural Network Ensembles against Deception: Ensemble Diversity, Accuracy and Robustness Ling Liu Wenqi Wei Ka-Ho Chow Margaret Loper Emre Gursoy Stacey Truex Yanzhao Wu UQCV AAML FedML 15 59 0 29 Aug 2019
A Statistical Defense Approach for Detecting Adversarial Examples Alessandro Cennamo Ido Freeman A. Kummert AAML 15 4 0 26 Aug 2019
Improving Adversarial Robustness via Attention and Adversarial Logit Pairing Dou Goodman Xingjian Li Ji Liu Jun Huan Tao Wei AAML 16 7 0 23 Aug 2019
Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples Marcus Soll Tobias Hinz S. Magg S. Wermter AAML 21 22 0 21 Aug 2019
Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks Ka-Ho Chow Wenqi Wei Yanzhao Wu Ling Liu AAML 25 15 0 21 Aug 2019
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Tianlin Li Siyue Wang Pin-Yu Chen Yanzhi Wang Brian Kulis Xue Lin S. Chin AAML 16 42 0 20 Aug 2019
Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Fnu Suya Jianfeng Chi David Evans Yuan Tian AAML 22 85 0 19 Aug 2019
Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks Jiadong Lin Chuanbiao Song Kun He Liwei Wang J. Hopcroft AAML 38 555 0 17 Aug 2019
DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation Seungju Cho Tae Joon Jun Byungsoo Oh Daeyoung Kim 27 31 0 14 Aug 2019
Once a MAN: Towards Multi-Target Attack via Learning Multi-Target Adversarial Network Once Jiangfan Han Xiaoyi Dong Ruimao Zhang Dongdong Chen Weiming Zhang Nenghai Yu Ping Luo Xiaogang Wang AAML 24 28 0 14 Aug 2019
Adversarial Neural Pruning with Latent Vulnerability Suppression Divyam Madaan Jinwoo Shin Sung Ju Hwang AAML 4 3 0 12 Aug 2019
Defending Against Adversarial Iris Examples Using Wavelet Decomposition Sobhan Soleymani Ali Dabouei J. Dawson Nasser M. Nasrabadi AAML 27 9 0 08 Aug 2019
Adversarial Self-Defense for Cycle-Consistent GANs D. Bashkirova Ben Usman Kate Saenko GAN 17 43 0 05 Aug 2019
Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve D. D. Thang Toshihiro Matsui AAML 11 4 0 05 Aug 2019
AdvGAN++ : Harnessing latent layers for adversary generation Puneet Mangla Surgan Jandial Sakshi Varshney V. Balasubramanian GAN 8 68 0 02 Aug 2019
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training Haichao Zhang Jianyu Wang AAML 23 230 0 24 Jul 2019
Joint Adversarial Training: Incorporating both Spatial and Pixel Attacks Haichao Zhang Jianyu Wang 22 4 0 24 Jul 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 30 445 0 24 Jul 2019
Towards Adversarially Robust Object Detection Haichao Zhang Jianyu Wang AAML ObjD 25 130 0 24 Jul 2019
Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving Yulong Cao Chaowei Xiao Benjamin Cyr Yimeng Zhou Wonseok Park Sara Rampazzi Qi Alfred Chen Kevin Fu Z. Morley Mao AAML 15 531 0 16 Jul 2019
Graph Interpolating Activation Improves Both Natural and Robust Accuracies in Data-Efficient Deep Learning Bao Wang Stanley J. Osher AAML AI4CE 42 10 0 16 Jul 2019
Recovery Guarantees for Compressible Signals with Adversarial Noise J. Dhaliwal Kyle Hambrook AAML 24 2 0 15 Jul 2019
Stateful Detection of Black-Box Adversarial Attacks Steven Chen Nicholas Carlini D. Wagner AAML MLAU 19 119 0 12 Jul 2019
Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference Yonatan Belinkov Adam Poliak Stuart M. Shieber Benjamin Van Durme Alexander M. Rush 27 94 0 09 Jul 2019
Affine Disentangled GAN for Interpretable and Robust AV Perception Letao Liu Martin Saerbeck Justin Dauwels 22 1 0 06 Jul 2019
Fooling a Real Car with Adversarial Traffic Signs N. Morgulis Alexander Kreines Shachar Mendelowitz Yuval Weisglass AAML 16 91 0 30 Jun 2019
Evolving Robust Neural Architectures to Defend from Adversarial Attacks Shashank Kotyan Danilo Vasconcellos Vargas OOD AAML 24 36 0 27 Jun 2019
Defending Adversarial Attacks by Correcting logits Yifeng Li Lingxi Xie Ya Zhang Rui Zhang Yanfeng Wang Qi Tian AAML 29 5 0 26 Jun 2019
Are Adversarial Perturbations a Showstopper for ML-Based CAD? A Case Study on CNN-Based Lithographic Hotspot Detection Kang Liu Haoyu Yang Yuzhe Ma Benjamin Tan Bei Yu Evangeline F. Y. Young Ramesh Karri S. Garg AAML 20 10 0 25 Jun 2019
Quantitative Verification of Neural Networks And its Security Applications Teodora Baluta Shiqi Shen Shweta Shinde Kuldeep S. Meel P. Saxena AAML 24 104 0 25 Jun 2019
The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks F. Assion Peter Schlicht Florens Greßner W. Günther Fabian Hüger Nico M. Schmidt Umair Rasheed AAML 25 14 0 17 Jun 2019
Defending Against Adversarial Attacks Using Random Forests Yifan Ding Liqiang Wang Huan Zhang Jinfeng Yi Deliang Fan Boqing Gong AAML 21 14 0 16 Jun 2019
Representation Quality Of Neural Networks Links To Adversarial Attacks and Defences Shashank Kotyan Danilo Vasconcellos Vargas Moe Matsuki 12 0 0 15 Jun 2019
Towards Compact and Robust Deep Neural Networks Vikash Sehwag Shiqi Wang Prateek Mittal Suman Jana AAML 35 40 0 14 Jun 2019
Copy and Paste: A Simple But Effective Initialization Method for Black-Box Adversarial Attacks T. Brunner Frederik Diehl Alois Knoll AAML 14 8 0 14 Jun 2019
$Adversarial Robustness Assessment: Why both $L_0$ and $L_\infty$ Attacks Are Necessary$ Adversarial Robustness Assessment: Why both $L_0$ and $L_\infty$ Attacks Are Necessary Shashank Kotyan Danilo Vasconcellos Vargas AAML 14 8 0 14 Jun 2019
Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks Ziang Yan Yiwen Guo Changshui Zhang AAML 33 110 0 11 Jun 2019
Intriguing properties of adversarial training at scale Cihang Xie Alan Yuille AAML 13 68 0 10 Jun 2019
Adversarial Attack Generation Empowered by Min-Max Optimization Jingkang Wang Tianyun Zhang Sijia Liu Pin-Yu Chen Jiacen Xu M. Fardad Yangqiu Song AAML 30 35 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
Strategies to architect AI Safety: Defense to guard AI from Adversaries R. A N. V AAML 17 0 0 08 Jun 2019
Defending Against Universal Attacks Through Selective Feature Regeneration Tejas S. Borkar Felix Heide Lina Karam AAML 23 1 0 08 Jun 2019
Efficient Project Gradient Descent for Ensemble Adversarial Attack Fanyou Wu R. Gazo E. Haviarova Bedrich Benes AAML 25 5 0 07 Jun 2019
A cryptographic approach to black box adversarial machine learning Kevin Shi Daniel J. Hsu Allison Bishop AAML 16 3 0 07 Jun 2019