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DeepFool: a simple and accurate method to fool deep neural networks

14 November 2015

Seyed-Mohsen Moosavi-Dezfooli

Papers citing "DeepFool: a simple and accurate method to fool deep neural networks"

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Title
High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks Haohan Wang Xindi Wu Pengcheng Yin Eric Xing 83 526 0 28 May 2019
Controlling Neural Level Sets Matan Atzmon Niv Haim Lior Yariv Ofer Israelov Haggai Maron Y. Lipman AI4CE 54 121 0 28 May 2019
Cross-Domain Transferability of Adversarial Perturbations Muzammal Naseer Salman H. Khan M. H. Khan Fahad Shahbaz Khan Fatih Porikli AAML 115 145 0 28 May 2019
Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss Pengcheng Li Jinfeng Yi Bowen Zhou Lijun Zhang AAML 65 37 0 28 May 2019
Label Universal Targeted Attack Naveed Akhtar M. Jalwana Bennamoun Ajmal Mian AAML 67 5 0 27 May 2019
Body Shape Privacy in Images: Understanding Privacy and Preventing Automatic Shape Extraction Hosnieh Sattar Katharina Krombholz Gerard Pons-Moll Mario Fritz 3DH 54 3 0 27 May 2019
GAT: Generative Adversarial Training for Adversarial Example Detection and Robust Classification Xuwang Yin Soheil Kolouri Gustavo K. Rohde AAML 106 44 0 27 May 2019
Non-Determinism in Neural Networks for Adversarial Robustness Daanish Ali Khan Linhong Li Ninghao Sha Zhuoran Liu Abelino Jiménez Bhiksha Raj Rita Singh OOD AAML 33 3 0 26 May 2019
Rearchitecting Classification Frameworks For Increased Robustness Varun Chandrasekaran Brian Tang Nicolas Papernot Kassem Fawaz S. Jha Xi Wu AAML OOD 100 8 0 26 May 2019
Generalizable Adversarial Attacks with Latent Variable Perturbation Modelling A. Bose Andre Cianflone William L. Hamilton OOD AAML 75 7 0 26 May 2019
Trust but Verify: An Information-Theoretic Explanation for the Adversarial Fragility of Machine Learning Systems, and a General Defense against Adversarial Attacks Jirong Yi Hui Xie Leixin Zhou Xiaodong Wu Weiyu Xu R. Mudumbai AAML 75 6 0 25 May 2019
Enhancing Adversarial Defense by k-Winners-Take-All Chang Xiao Peilin Zhong Changxi Zheng AAML 80 99 0 25 May 2019
Not All Features Are Equal: Feature Leveling Deep Neural Networks for Better Interpretation Yingjing Lu Runde Yang MILM 39 2 0 24 May 2019
Adversarially Robust Distillation Micah Goldblum Liam H. Fowl Soheil Feizi Tom Goldstein AAML 87 213 0 23 May 2019
Ensemble Model Patching: A Parameter-Efficient Variational Bayesian Neural Network Oscar Chang Yuling Yao David Williams-King Hod Lipson BDL UQCV 71 8 0 23 May 2019
Convergence and Margin of Adversarial Training on Separable Data Zachary B. Charles Shashank Rajput S. Wright Dimitris Papailiopoulos AAML 71 17 0 22 May 2019
Detecting Adversarial Examples and Other Misclassifications in Neural Networks by Introspection Jonathan Aigrain Marcin Detyniecki AAML 70 30 0 22 May 2019
A framework for the extraction of Deep Neural Networks by leveraging public data Soham Pal Yash Gupta Aditya Shukla Aditya Kanade S. Shevade V. Ganapathy FedML MLAU MIACV 82 56 0 22 May 2019
Adversarially robust transfer learning Ali Shafahi Parsa Saadatpanah Chen Zhu Amin Ghiasi Christoph Studer David Jacobs Tom Goldstein OOD 52 117 0 20 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 114 18 0 19 May 2019
What Do Adversarially Robust Models Look At? Takahiro Itazuri Yoshihiro Fukuhara Hirokatsu Kataoka Shigeo Morishima 32 5 0 19 May 2019
Simple Black-box Adversarial Attacks Chuan Guo Jacob R. Gardner Yurong You A. Wilson Kilian Q. Weinberger AAML 78 581 0 17 May 2019
Fooling Computer Vision into Inferring the Wrong Body Mass Index Owen Levin Zihang Meng Vikas Singh Xiaojin Zhu AAML 11 3 0 16 May 2019
On Norm-Agnostic Robustness of Adversarial Training Bai Li Changyou Chen Wenlin Wang Lawrence Carin OOD SILM 68 7 0 15 May 2019
An Efficient Pre-processing Method to Eliminate Adversarial Effects Hua Wang Jie Wang Z. Yin AAML 38 1 0 15 May 2019
Moving Target Defense for Deep Visual Sensing against Adversarial Examples Qun Song Zhenyu Yan Rui Tan AAML 45 20 0 11 May 2019
On the Connection Between Adversarial Robustness and Saliency Map Interpretability Christian Etmann Sebastian Lunz Peter Maass Carola-Bibiane Schönlieb AAML FAtt 63 162 0 10 May 2019
Exact Adversarial Attack to Image Captioning via Structured Output Learning with Latent Variables Yan Xu Baoyuan Wu Fumin Shen Yanbo Fan Yong Zhang Heng Tao Shen Wei Liu AAML 78 56 0 10 May 2019
Exploring the Hyperparameter Landscape of Adversarial Robustness Evelyn Duesterwald Anupama Murthi Ganesh Venkataraman M. Sinn Deepak Vijaykeerthy AAML 52 7 0 09 May 2019
Universal Adversarial Perturbations for Speech Recognition Systems Paarth Neekhara Shehzeen Samarah Hussain Prakhar Pandey Shlomo Dubnov Julian McAuley F. Koushanfar AAML 82 118 0 09 May 2019
ROSA: Robust Salient Object Detection against Adversarial Attacks Haofeng Li Guanbin Li Yizhou Yu AAML 70 29 0 09 May 2019
AI Enabling Technologies: A Survey V. Gadepally Justin A. Goodwin J. Kepner Albert Reuther Hayley Reynolds S. Samsi Jonathan Su David Martinez 43 25 0 08 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 77 26 0 05 May 2019
When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing Z. Luo Shangqing Zhao Zhuo Lu Jie Xu Y. Sagduyu AAML 89 53 0 04 May 2019
CharBot: A Simple and Effective Method for Evading DGA Classifiers Jonathan Peck Claire Nie R. Sivaguru Charles Grumer Femi G. Olumofin Bin Yu A. Nascimento Martine De Cock AAML 45 44 0 03 May 2019
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle Dinghuai Zhang Tianyuan Zhang Yiping Lu Zhanxing Zhu Bin Dong AAML 130 362 0 02 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 86 245 0 01 May 2019
POBA-GA: Perturbation Optimized Black-Box Adversarial Attacks via Genetic Algorithm Jinyin Chen Mengmeng Su Shijing Shen Hui Xiong Haibin Zheng AAML 124 68 0 01 May 2019
Test Selection for Deep Learning Systems Wei Ma Mike Papadakis Anestis Tsakmalis Maxime Cordy Yves Le Traon OOD 70 93 0 30 Apr 2019
Property Inference for Deep Neural Networks D. Gopinath Hayes Converse C. Păsăreanu Ankur Taly 74 8 0 29 Apr 2019
Analytical Moment Regularizer for Gaussian Robust Networks Modar Alfadly Adel Bibi Guohao Li AAML 29 4 0 24 Apr 2019
AnonymousNet: Natural Face De-Identification with Measurable Privacy Tao Li Lei Lin PICV 82 147 0 19 Apr 2019
Fooling automated surveillance cameras: adversarial patches to attack person detection Simen Thys W. V. Ranst Toon Goedemé AAML 110 571 0 18 Apr 2019
ZK-GanDef: A GAN based Zero Knowledge Adversarial Training Defense for Neural Networks Guanxiong Liu Issa M. Khalil Abdallah Khreishah AAML 50 18 0 17 Apr 2019
Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers Ameya Joshi Amitangshu Mukherjee Soumik Sarkar Chinmay Hegde AAML 92 100 0 17 Apr 2019
Interpreting Adversarial Examples with Attributes Sadaf Gulshad J. H. Metzen A. Smeulders Zeynep Akata FAtt AAML 93 6 0 17 Apr 2019
Adversarial Defense Through Network Profiling Based Path Extraction Yuxian Qiu Jingwen Leng Cong Guo Quan Chen Chong Li Minyi Guo Yuhao Zhu AAML 69 51 0 17 Apr 2019
Detecting the Unexpected via Image Resynthesis Krzysztof Lis Krishna Kanth Nakka Pascal Fua Mathieu Salzmann UQCV 82 178 0 16 Apr 2019
Generating Minimal Adversarial Perturbations with Integrated Adaptive Gradients Yatie Xiao Chi-Man Pun AAML GAN TTA 21 0 0 12 Apr 2019
Deep learning as optimal control problems: models and numerical methods Martin Benning E. Celledoni Matthias Joachim Ehrhardt B. Owren Carola-Bibiane Schönlieb 137 81 0 11 Apr 2019