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Explaining and Harnessing Adversarial Examples

20 December 2014

Papers citing "Explaining and Harnessing Adversarial Examples"

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Title
How intelligent are convolutional neural networks? Zhennan Yan Xiangmin Zhou 64 11 0 18 Sep 2017
Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification Xiaoyu Cao Neil Zhenqiang Gong AAML 85 212 0 17 Sep 2017
Embedding Deep Networks into Visual Explanations Zhongang Qi Saeed Khorram Fuxin Li 41 27 0 15 Sep 2017
Learning Functional Causal Models with Generative Neural Networks Hugo Jair Escalante Sergio Escalera Xavier Baro Isabelle M Guyon Umut Güçlü Marcel van Gerven CML BDL 107 108 0 15 Sep 2017
Denoising Autoencoders for Overgeneralization in Neural Networks G. Spigler UQCV AI4CE 61 27 0 14 Sep 2017
REMOTEGATE: Incentive-Compatible Remote Configuration of Security Gateways Abhinav Aggarwal M. Zamani Mihai Christodorescu 73 0 0 14 Sep 2017
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples Pin-Yu Chen Yash Sharma Huan Zhang Jinfeng Yi Cho-Jui Hsieh AAML 80 641 0 13 Sep 2017
Can Deep Neural Networks Match the Related Objects?: A Survey on ImageNet-trained Classification Models Han S. Lee Heechul Jung Alex A. Agarwal Junmo Kim 85 6 0 12 Sep 2017
Art of singular vectors and universal adversarial perturbations Valentin Khrulkov Ivan Oseledets AAML 78 132 0 11 Sep 2017
Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks Thilo Strauss Markus Hanselmann Andrej Junginger Holger Ulmer AAML 93 137 0 11 Sep 2017
Towards Proving the Adversarial Robustness of Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel J. Kochenderfer AAML OOD 107 118 0 08 Sep 2017
DeepFense: Online Accelerated Defense Against Adversarial Deep Learning B. Rouhani Mohammad Samragh Mojan Javaheripi T. Javidi F. Koushanfar AAML 53 15 0 08 Sep 2017
Deep and Confident Prediction for Time Series at Uber Lingxue Zhu N. Laptev BDL AI4TS 191 345 0 06 Sep 2017
Learning to Compose Domain-Specific Transformations for Data Augmentation Alexander J. Ratner Henry R. Ehrenberg Zeshan Hussain Jared A. Dunnmon Christopher Ré 90 351 0 06 Sep 2017
Unsupervised feature learning with discriminative encoder Gaurav Pandey Ambedkar Dukkipati SSL 60 6 0 03 Sep 2017
Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization Luis Muñoz-González Battista Biggio Ambra Demontis Andrea Paudice Vasin Wongrassamee Emil C. Lupu Fabio Roli AAML 140 633 0 29 Aug 2017
DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars Yuchi Tian Kexin Pei Suman Jana Baishakhi Ray AAML 99 1,365 0 28 Aug 2017
Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved Features Liang Tong Yue Liu Chen Hajaj Chaowei Xiao Ning Zhang Yevgeniy Vorobeychik AAML OOD 52 88 0 28 Aug 2017
Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid Marco Melis Ambra Demontis Battista Biggio Gavin Brown Giorgio Fumera Fabio Roli AAML 79 98 0 23 Aug 2017
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain Tianyu Gu Brendan Dolan-Gavitt S. Garg SILM 149 1,786 0 22 Aug 2017
What does 2D geometric information really tell us about 3D face shape? Anil Bas W. Smith 3DH CVBM 3DV 85 24 0 22 Aug 2017
CNN Fixations: An unraveling approach to visualize the discriminative image regions Konda Reddy Mopuri Utsav Garg R. Venkatesh Babu AAML 91 56 0 22 Aug 2017
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples Yinpeng Dong Hang Su Jun Zhu Fan Bao AAML 143 129 0 18 Aug 2017
A deep architecture for unified aesthetic prediction Naila Murray Albert Gordo 88 47 0 16 Aug 2017
Attacking Automatic Video Analysis Algorithms: A Case Study of Google Cloud Video Intelligence API Hossein Hosseini Baicen Xiao Andrew Clark Radha Poovendran AAML 65 24 0 14 Aug 2017
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models Pin-Yu Chen Huan Zhang Yash Sharma Jinfeng Yi Cho-Jui Hsieh AAML 115 1,894 0 14 Aug 2017
Cascade Adversarial Machine Learning Regularized with a Unified Embedding Taesik Na J. Ko Saibal Mukhopadhyay AAML GAN 95 102 0 08 Aug 2017
MHTN: Modal-adversarial Hybrid Transfer Network for Cross-modal Retrieval Xin Huang Yuxin Peng Mingkuan Yuan GAN 72 111 0 08 Aug 2017
Adversarial Robustness: Softmax versus Openmax Andras Rozsa Manuel Günther Terrance E. Boult AAML 60 32 0 05 Aug 2017
Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning Andrew P. Norton Yanjun Qi AAML 75 47 0 01 Aug 2017
Photographic Image Synthesis with Cascaded Refinement Networks Qifeng Chen V. Koltun 77 954 0 28 Jul 2017
Robust Physical-World Attacks on Deep Learning Models Kevin Eykholt Ivan Evtimov Earlence Fernandes Yue Liu Amir Rahmati Chaowei Xiao Atul Prakash Tadayoshi Kohno Basel Alomair AAML 143 595 0 27 Jul 2017
Adversarial Examples for Evaluating Reading Comprehension Systems Robin Jia Percy Liang AAML ELM 241 1,610 0 23 Jul 2017
Confidence estimation in Deep Neural networks via density modelling Akshayvarun Subramanya Suraj Srinivas R. Venkatesh Babu 65 51 0 21 Jul 2017
Efficient Defenses Against Adversarial Attacks Valentina Zantedeschi Maria-Irina Nicolae Ambrish Rawat AAML 74 297 0 21 Jul 2017
Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers Ishai Rosenberg A. Shabtai Lior Rokach Yuval Elovici AAML 138 48 0 19 Jul 2017
Fast Feature Fool: A data independent approach to universal adversarial perturbations Konda Reddy Mopuri Utsav Garg R. Venkatesh Babu AAML 132 205 0 18 Jul 2017
APE-GAN: Adversarial Perturbation Elimination with GAN Shiwei Shen Guoqing Jin Feng Dai Yongdong Zhang GAN 122 221 0 18 Jul 2017
Houdini: Fooling Deep Structured Prediction Models Moustapha Cissé Yossi Adi Natalia Neverova Joseph Keshet AAML 90 272 0 17 Jul 2017
Trial without Error: Towards Safe Reinforcement Learning via Human Intervention William Saunders Girish Sastry Andreas Stuhlmuller Owain Evans OffRL 79 231 0 17 Jul 2017
Foolbox: A Python toolbox to benchmark the robustness of machine learning models Jonas Rauber Wieland Brendel Matthias Bethge AAML 82 283 0 13 Jul 2017
Adversarial Dropout for Supervised and Semi-supervised Learning Sungrae Park Jun-Keon Park Su-Jin Shin Il-Chul Moon GAN 99 174 0 12 Jul 2017
NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles Jiajun Lu Hussein Sibai Evan Fabry David A. Forsyth AAML 104 282 0 12 Jul 2017
A Survey on Resilient Machine Learning Atul Kumar S. Mehta OOD AAML 83 16 0 11 Jul 2017
Towards Crafting Text Adversarial Samples Suranjana Samanta S. Mehta AAML 85 222 0 10 Jul 2017
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks John Bradshaw A. G. Matthews Zoubin Ghahramani BDL AAML 123 172 0 08 Jul 2017
Learning Loss Functions for Semi-supervised Learning via Discriminative Adversarial Networks Cicero Nogueira dos Santos Kahini Wadhawan Bowen Zhou GAN 95 30 0 07 Jul 2017
UPSET and ANGRI : Breaking High Performance Image Classifiers Sayantan Sarkar Ankan Bansal U. Mahbub Rama Chellappa AAML 83 108 0 04 Jul 2017
Spectrally-normalized margin bounds for neural networks Peter L. Bartlett Dylan J. Foster Matus Telgarsky ODL 330 1,225 0 26 Jun 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 381 12,169 0 19 Jun 2017