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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
Pick-Object-Attack: Type-Specific Adversarial Attack for Object Detection Omid Mohamad Nezami Akshay Chaturvedi Mark Dras Utpal Garain AAML ObjD 61 19 0 05 Jun 2020
Towards Understanding Fast Adversarial Training Bai Li Shiqi Wang Suman Jana Lawrence Carin AAML 78 50 0 04 Jun 2020
SearchFromFree: Adversarial Measurements for Machine Learning-based Energy Theft Detection Jiangnan Li Yingyuan Yang Jinyuan Stella Sun AAML 89 21 0 02 Jun 2020
Perturbation Analysis of Gradient-based Adversarial Attacks Utku Ozbulak Manvel Gasparyan W. D. Neve Arnout Van Messem AAML 34 7 0 02 Jun 2020
Exploring the role of Input and Output Layers of a Deep Neural Network in Adversarial Defense Jay N. Paranjape R. Dubey Vijendran V. Gopalan AAML 47 2 0 02 Jun 2020
Exploring Model Robustness with Adaptive Networks and Improved Adversarial Training Zheng Xu Ali Shafahi Tom Goldstein AAML 51 2 0 30 May 2020
Adversarial Classification via Distributional Robustness with Wasserstein Ambiguity Nam Ho-Nguyen Stephen J. Wright OOD 112 17 0 28 May 2020
Mitigating Advanced Adversarial Attacks with More Advanced Gradient Obfuscation Techniques Han Qiu Yi Zeng Qinkai Zheng Tianwei Zhang Meikang Qiu G. Memmi AAML 69 14 0 27 May 2020
Enhancing Resilience of Deep Learning Networks by Means of Transferable Adversaries M. Seiler Heike Trautmann P. Kerschke AAML 24 0 0 27 May 2020
A Protection against the Extraction of Neural Network Models H. Chabanne Vincent Despiegel Linda Guiga FedML 83 5 0 26 May 2020
Adaptive Adversarial Logits Pairing Shangxi Wu Jitao Sang Kaiyan Xu Guanhua Zheng Changsheng Xu AAML 31 3 0 25 May 2020
ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds Kibok Lee Zhuoyuan Chen Xinchen Yan R. Urtasun Ersin Yumer 3DPC 66 32 0 24 May 2020
Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks Hokuto Hirano K. Koga Kazuhiro Takemoto AAML 114 49 0 22 May 2020
Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack Hakmin Lee Hong Joo Lee S. T. Kim Yong Man Ro FedML OOD AAML 53 10 0 21 May 2020
Revisiting Role of Autoencoders in Adversarial Settings Byeong Cheon Kim Jung Uk Kim Hakmin Lee Yong Man Ro AAML 11 4 0 21 May 2020
Model-Based Robust Deep Learning: Generalizing to Natural, Out-of-Distribution Data Alexander Robey Hamed Hassani George J. Pappas OOD 103 43 0 20 May 2020
Synthesizing Unrestricted False Positive Adversarial Objects Using Generative Models Martin Kotuliak Sandro Schönborn Andrei Dan GAN AAML 36 1 0 19 May 2020
On Intrinsic Dataset Properties for Adversarial Machine Learning J. Z. Pan Nicholas Zufelt AAML 40 1 0 19 May 2020
Universalization of any adversarial attack using very few test examples Sandesh Kamath Amit Deshpande K. Subrahmanyam Vineeth N. Balasubramanian FedML AAML 36 1 0 18 May 2020
Toward Adversarial Robustness by Diversity in an Ensemble of Specialized Deep Neural Networks Mahdieh Abbasi Arezoo Rajabi Christian Gagné R. Bobba AAML 39 15 0 17 May 2020
Universal Adversarial Perturbations: A Survey Ashutosh Chaubey Nikhil Agrawal Kavya Barnwal K. K. Guliani Pramod Mehta OOD AAML 107 47 0 16 May 2020
A Deep Learning-based Fine-grained Hierarchical Learning Approach for Robust Malware Classification Ahmed A. Abusnaina Mohammed Abuhamad Hisham Alasmary Afsah Anwar Rhongho Jang Saeed Salem Daehun Nyang David A. Mohaisen AAML 9 5 0 14 May 2020
DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses Yaxin Li Wei Jin Han Xu Jiliang Tang AAML 90 133 0 13 May 2020
Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients Chengcheng Ma Baoyuan Wu Shibiao Xu Yanbo Fan Yong Zhang Xiaopeng Zhang Zhifeng Li AAML 62 9 0 12 May 2020
Towards Robustness against Unsuspicious Adversarial Examples Liang Tong Minzhe Guo A. Prakash Yevgeniy Vorobeychik AAML 21 0 0 08 May 2020
Projection & Probability-Driven Black-Box Attack Jie Li Rongrong Ji Hong Liu Jianzhuang Liu Bineng Zhong Cheng Deng Q. Tian AAML 72 49 0 08 May 2020
Lifted Regression/Reconstruction Networks R. Høier Christopher Zach 31 7 0 07 May 2020
A Review of Computer Vision Methods in Network Security Jiawei Zhao Rahat Masood Suranga Seneviratne AAML 50 48 0 07 May 2020
GraCIAS: Grassmannian of Corrupted Images for Adversarial Security Ankita Shukla Pavan Turaga Saket Anand AAML 39 1 0 06 May 2020
Testing the Robustness of AutoML Systems Tuomas Halvari J. Nurminen T. Mikkonen 24 12 0 06 May 2020
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder Guanlin Li Shuya Ding Jun Luo Chang-rui Liu AAML 107 19 0 06 May 2020
Jacks of All Trades, Masters Of None: Addressing Distributional Shift and Obtrusiveness via Transparent Patch Attacks Neil Fendley M. Lennon I-J. Wang Philippe Burlina Nathan G. Drenkow 21 7 0 01 May 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 118 382 0 30 Apr 2020
Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability Nathan Inkawhich Kevin J. Liang Binghui Wang Matthew J. Inkawhich Lawrence Carin Yiran Chen AAML 87 90 0 29 Apr 2020
Adversarial Fooling Beyond "Flipping the Label" Konda Reddy Mopuri Vaisakh Shaj R. Venkatesh Babu AAML 67 12 0 27 Apr 2020
An Epistemic Approach to the Formal Specification of Statistical Machine Learning Yusuke Kawamoto CML 44 5 0 27 Apr 2020
Transferable Perturbations of Deep Feature Distributions Nathan Inkawhich Kevin J. Liang Lawrence Carin Yiran Chen AAML 73 87 0 27 Apr 2020
Printing and Scanning Attack for Image Counter Forensics Hailey James O. Gupta D. Raviv AAML 39 3 0 27 Apr 2020
Harnessing adversarial examples with a surprisingly simple defense Ali Borji AAML 31 0 0 26 Apr 2020
Enabling Fast and Universal Audio Adversarial Attack Using Generative Model Yi Xie Zhuohang Li Cong Shi Jian-Dong Liu Yingying Chen Bo Yuan AAML 87 69 0 26 Apr 2020
Improved Adversarial Training via Learned Optimizer Yuanhao Xiong Cho-Jui Hsieh AAML 77 31 0 25 Apr 2020
A Black-box Adversarial Attack Strategy with Adjustable Sparsity and Generalizability for Deep Image Classifiers Arka Ghosh S. S. Mullick Shounak Datta Swagatam Das R. Mallipeddi A. Das AAML 63 38 0 24 Apr 2020
Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty Xiyue Zhang Xiaofei Xie Lei Ma Xiaoning Du Q. Hu Yang Liu Jianjun Zhao Meng Sun AAML 60 79 0 24 Apr 2020
RAIN: A Simple Approach for Robust and Accurate Image Classification Networks Jiawei Du Hanshu Yan Vincent Y. F. Tan Qiufeng Wang Rick Siow Mong Goh Jiashi Feng AAML 16 0 0 24 Apr 2020
Adversarial Machine Learning in Network Intrusion Detection Systems Elie Alhajjar P. Maxwell Nathaniel D. Bastian GAN SILM AAML 105 141 0 23 Apr 2020
Discovering Imperfectly Observable Adversarial Actions using Anomaly Detection Olga Petrova K. Durkota Galina Alperovich Karel Horak Michal Najman B. Bosanský Viliam Lisý AAML 16 1 0 22 Apr 2020
Certifying Joint Adversarial Robustness for Model Ensembles M. Jonas David Evans AAML 68 2 0 21 Apr 2020
Headless Horseman: Adversarial Attacks on Transfer Learning Models Ahmed Abdelkader Michael J. Curry Liam H. Fowl Tom Goldstein Avi Schwarzschild Manli Shu Christoph Studer Chen Zhu 64 5 0 20 Apr 2020
Single-step Adversarial training with Dropout Scheduling S. VivekB. R. Venkatesh Babu OOD AAML 65 73 0 18 Apr 2020
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space Dongyu Ru Yating Luo Lin Qiu Hao Zhou Mingxuan Wang Weinan Zhang Yong Yu Lei Li 73 29 0 17 Apr 2020