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Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

1 February 2018

Papers citing "Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples"

50 / 1,929 papers shown

Title
Deep Neural Rejection against Adversarial Examples Angelo Sotgiu Ambra Demontis Marco Melis Battista Biggio Giorgio Fumera Xiaoyi Feng Fabio Roli AAML 88 69 0 01 Oct 2019
Role of Spatial Context in Adversarial Robustness for Object Detection Aniruddha Saha Akshayvarun Subramanya Koninika Patil Hamed Pirsiavash ObjD AAML 112 54 0 30 Sep 2019
Deep k-NN Defense against Clean-label Data Poisoning Attacks Neehar Peri Neal Gupta Wenjie Huang Liam H. Fowl Chen Zhu Soheil Feizi Tom Goldstein John P. Dickerson AAML 71 6 0 29 Sep 2019
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts Yu Sun Xiaolong Wang Zhuang Liu John Miller Alexei A. Efros Moritz Hardt TTA OOD 99 96 0 29 Sep 2019
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks Rémi Bernhard Pierre-Alain Moëllic J. Dutertre AAML MQ 98 18 0 27 Sep 2019
Lower Bounds on Adversarial Robustness from Optimal Transport A. Bhagoji Daniel Cullina Prateek Mittal OOD OT AAML 72 94 0 26 Sep 2019
FreeLB: Enhanced Adversarial Training for Natural Language Understanding Chen Zhu Yu Cheng Zhe Gan S. Sun Tom Goldstein Jingjing Liu AAML 296 443 0 25 Sep 2019
Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks Tianyu Pang Kun Xu Jun Zhu AAML 91 105 0 25 Sep 2019
Sign-OPT: A Query-Efficient Hard-label Adversarial Attack Minhao Cheng Simranjit Singh Patrick H. Chen Pin-Yu Chen Sijia Liu Cho-Jui Hsieh AAML 237 224 0 24 Sep 2019
MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples Jinyuan Jia Ahmed Salem Michael Backes Yang Zhang Neil Zhenqiang Gong 108 398 0 23 Sep 2019
FENCE: Feasible Evasion Attacks on Neural Networks in Constrained Environments Alesia Chernikova Alina Oprea AAML 121 40 0 23 Sep 2019
Robust Local Features for Improving the Generalization of Adversarial Training Chuanbiao Song Kun He Jiadong Lin Liwei Wang John E. Hopcroft OOD AAML 75 70 0 23 Sep 2019
Defending Against Physically Realizable Attacks on Image Classification Tong Wu Liang Tong Yevgeniy Vorobeychik AAML 84 127 0 20 Sep 2019
Training Robust Deep Neural Networks via Adversarial Noise Propagation Aishan Liu Xianglong Liu Chongzhi Zhang Hang Yu Qiang Liu Dacheng Tao AAML 86 116 0 19 Sep 2019
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks Sekitoshi Kanai Yasutoshi Ida Yasuhiro Fujiwara Masanori Yamada S. Adachi AAML 44 1 0 19 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 79 680 0 17 Sep 2019
HAD-GAN: A Human-perception Auxiliary Defense GAN to Defend Adversarial Examples Wanting Yu Hongyi Yu Lingyun Jiang Mengli Zhang Kai Qiao GAN AAML 27 0 0 17 Sep 2019
Interpreting and Improving Adversarial Robustness of Deep Neural Networks with Neuron Sensitivity Chongzhi Zhang Aishan Liu Xianglong Liu Yitao Xu Hang Yu Yuqing Ma Tianlin Li AAML 134 19 0 16 Sep 2019
Natural Language Adversarial Defense through Synonym Encoding Xiaosen Wang Hao Jin Yichen Yang Kun He AAML 91 64 0 15 Sep 2019
White-Box Adversarial Defense via Self-Supervised Data Estimation Zudi Lin Hanspeter Pfister Ziming Zhang AAML 23 2 0 13 Sep 2019
Towards Model-Agnostic Adversarial Defenses using Adversarially Trained Autoencoders Pratik Vaishnavi Kevin Eykholt A. Prakash Amir Rahmati AAML 46 2 0 12 Sep 2019
Feedback Learning for Improving the Robustness of Neural Networks Chang Song Zuoguan Wang H. Li AAML 65 7 0 12 Sep 2019
Sparse and Imperceivable Adversarial Attacks Francesco Croce Matthias Hein AAML 110 199 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 77 10 0 11 Sep 2019
Identifying and Resisting Adversarial Videos Using Temporal Consistency Xiaojun Jia Xingxing Wei Xiaochun Cao AAML 42 15 0 11 Sep 2019
Neural Belief Reasoner Haifeng Qian NAI BDL 26 1 0 10 Sep 2019
FDA: Feature Disruptive Attack Aditya Ganeshan S. VivekB. R. Venkatesh Babu AAML 118 105 0 10 Sep 2019
Learning to Disentangle Robust and Vulnerable Features for Adversarial Detection Byunggill Joe Sung Ju Hwang I. Shin AAML 35 1 0 10 Sep 2019
Adversarial Robustness Against the Union of Multiple Perturbation Models Pratyush Maini Eric Wong J. Zico Kolter OOD AAML 65 151 0 09 Sep 2019
On the Need for Topology-Aware Generative Models for Manifold-Based Defenses Uyeong Jang Susmit Jha S. Jha AAML 70 13 0 07 Sep 2019
Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation Po-Sen Huang Robert Stanforth Johannes Welbl Chris Dyer Dani Yogatama Sven Gowal Krishnamurthy Dvijotham Pushmeet Kohli AAML 112 166 0 03 Sep 2019
Certified Robustness to Adversarial Word Substitutions Robin Jia Aditi Raghunathan Kerem Göksel Percy Liang AAML 351 294 0 03 Sep 2019
Metric Learning for Adversarial Robustness Chengzhi Mao Ziyuan Zhong Junfeng Yang Carl Vondrick Baishakhi Ray OOD 96 188 0 03 Sep 2019
The many faces of deep learning Raul Vicente FedML AI4CE 50 0 0 25 Aug 2019
Improving Adversarial Robustness via Attention and Adversarial Logit Pairing Dou Goodman Xingjian Li Ji Liu Jun Huan Tao Wei AAML 43 7 0 23 Aug 2019
Testing Robustness Against Unforeseen Adversaries Maximilian Kaufmann Daniel Kang Yi Sun Steven Basart Xuwang Yin ... Adam Dziedzic Franziska Boenisch Tom B. Brown Jacob Steinhardt Dan Hendrycks AAML 32 0 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 62 42 0 20 Aug 2019
Adversarial Defense by Suppressing High-frequency Components Zhendong Zhang Cheolkon Jung X. Liang 83 24 0 19 Aug 2019
Verification of Neural Network Control Policy Under Persistent Adversarial Perturbation Yuh-Shyang Wang Tsui-Wei Weng Luca Daniel AAML 57 16 0 18 Aug 2019
Implicit Deep Learning L. Ghaoui Fangda Gu Bertrand Travacca Armin Askari Alicia Y. Tsai AI4CE 64 182 0 17 Aug 2019
Adversarial Neural Pruning with Latent Vulnerability Suppression Divyam Madaan Jinwoo Shin Sung Ju Hwang AAML 18 3 0 12 Aug 2019
BlurNet: Defense by Filtering the Feature Maps Ravi Raju Mikko H. Lipasti AAML 69 16 0 06 Aug 2019
MetaAdvDet: Towards Robust Detection of Evolving Adversarial Attacks Chen Ma Chenxu Zhao Hailin Shi Li Chen Junhai Yong Dan Zeng AAML 55 17 0 06 Aug 2019
A principled approach for generating adversarial images under non-smooth dissimilarity metrics Aram-Alexandre Pooladian Chris Finlay Tim Hoheisel Adam M. Oberman AAML 54 3 0 05 Aug 2019
Not All Adversarial Examples Require a Complex Defense: Identifying Over-optimized Adversarial Examples with IQR-based Logit Thresholding Utku Ozbulak Arnout Van Messem W. D. Neve AAML 34 1 0 30 Jul 2019
Are Odds Really Odd? Bypassing Statistical Detection of Adversarial Examples Hossein Hosseini Sreeram Kannan Radha Poovendran AAML 70 19 0 28 Jul 2019
Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin Kaiwen Wu Yaoliang Yu AAML 45 8 0 26 Jul 2019
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training Haichao Zhang Jianyu Wang AAML 112 231 0 24 Jul 2019
Joint Adversarial Training: Incorporating both Spatial and Pixel Attacks Haichao Zhang Jianyu Wang 72 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 93 459 0 24 Jul 2019