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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
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 219 2,057 0 08 Feb 2019
Robustness Of Saak Transform Against Adversarial Attacks T. Ramanathan Abinaya Manimaran Suya You C.-C. Jay Kuo 76 5 0 07 Feb 2019
Daedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial Examples Derui Wang Chaoran Li S. Wen Qing-Long Han Surya Nepal Xiangyu Zhang Yang Xiang AAML 75 40 0 06 Feb 2019
Theoretical evidence for adversarial robustness through randomization Rafael Pinot Laurent Meunier Alexandre Araujo H. Kashima Florian Yger Cédric Gouy-Pailler Jamal Atif AAML 110 83 0 04 Feb 2019
Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacks S. Saralajew Lars Holdijk Maike Rees T. Villmann OOD 49 19 0 01 Feb 2019
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation Sahil Singla Eric Wallace Shi Feng Soheil Feizi FAtt 71 59 0 01 Feb 2019
Robustness Certificates Against Adversarial Examples for ReLU Networks Sahil Singla Soheil Feizi AAML 68 21 0 01 Feb 2019
Training Artificial Neural Networks by Generalized Likelihood Ratio Method: Exploring Brain-like Learning to Improve Robustness Li Xiao Yijie Peng J. Hong Zewu Ke Shuhuai Yang 8 0 0 31 Jan 2019
Augmenting Model Robustness with Transformation-Invariant Attacks Houpu Yao Zhe Wang Guangyu Nie Yassine Mazboudi Yezhou Yang Yi Ren AAML OOD 31 3 0 31 Jan 2019
Who's Afraid of Adversarial Queries? The Impact of Image Modifications on Content-based Image Retrieval Zhuoran Liu Zhengyu Zhao Martha Larson AAML 79 43 0 29 Jan 2019
A Black-box Attack on Neural Networks Based on Swarm Evolutionary Algorithm Xiaolei Liu Yuheng Luo Xiaosong Zhang Qingxin Zhu AAML 53 16 0 26 Jan 2019
Generative Adversarial Networks for Black-Box API Attacks with Limited Training Data Yi Shi Y. Sagduyu Kemal Davaslioglu Jason H. Li AAML 58 29 0 25 Jan 2019
Theoretically Principled Trade-off between Robustness and Accuracy Hongyang R. Zhang Yaodong Yu Jiantao Jiao Eric Xing L. Ghaoui Michael I. Jordan 251 2,566 0 24 Jan 2019
Sitatapatra: Blocking the Transfer of Adversarial Samples Ilia Shumailov Xitong Gao Yiren Zhao Robert D. Mullins Ross J. Anderson Chengzhong Xu AAML GAN 64 14 0 23 Jan 2019
Sensitivity Analysis of Deep Neural Networks Hai Shu Hongtu Zhu AAML 46 53 0 22 Jan 2019
Universal Rules for Fooling Deep Neural Networks based Text Classification Di Li Danilo Vasconcellos Vargas Kouichi Sakurai AAML 46 11 0 22 Jan 2019
Perception-in-the-Loop Adversarial Examples Mahmoud Salamati Sadegh Soudjani R. Majumdar AAML 20 2 0 21 Jan 2019
Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey W. Zhang Quan Z. Sheng A. Alhazmi Chenliang Li AAML 125 57 0 21 Jan 2019
Generating Adversarial Perturbation with Root Mean Square Gradient Yatie Xiao Chi-Man Pun Jizhe Zhou GAN 33 1 0 13 Jan 2019
ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification System Huangxun Chen Chenyu Huang Qianyi Huang Qian Zhang Wei Wang AAML 75 28 0 12 Jan 2019
Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers Daniel Liu Ronald Yu Hao Su 3DPC 97 170 0 10 Jan 2019
Thinking Outside the Pool: Active Training Image Creation for Relative Attributes Aron Yu Kristen Grauman 51 23 0 08 Jan 2019
Contamination Attacks and Mitigation in Multi-Party Machine Learning Jamie Hayes O. Ohrimenko AAML FedML 114 75 0 08 Jan 2019
Interpretable BoW Networks for Adversarial Example Detection Krishna Kanth Nakka Mathieu Salzmann GAN AAML 33 0 0 08 Jan 2019
Ten ways to fool the masses with machine learning F. Minhas Amina Asif Asa Ben-Hur FedML HAI 68 5 0 07 Jan 2019
Image Super-Resolution as a Defense Against Adversarial Attacks Aamir Mustafa Salman H. Khan Munawar Hayat Jianbing Shen Ling Shao AAML SupR 100 176 0 07 Jan 2019
Adversarial Examples Versus Cloud-based Detectors: A Black-box Empirical Study Xurong Li S. Ji Men Han Juntao Ji Zhenyu Ren Yushan Liu Chunming Wu AAML 93 31 0 04 Jan 2019
Multi-Label Adversarial Perturbations Qingquan Song Haifeng Jin Xiao Huang Helen Zhou AAML 63 37 0 02 Jan 2019
A Noise-Sensitivity-Analysis-Based Test Prioritization Technique for Deep Neural Networks Long Zhang Xuechao Sun Yong Li Zhenyu Zhang AAML 53 22 0 01 Jan 2019
DeepBillboard: Systematic Physical-World Testing of Autonomous Driving Systems Husheng Zhou Wei Li Yuankun Zhu Yuqun Zhang Bei Yu Lingming Zhang Cong Liu AAML 85 179 0 27 Dec 2018
End-to-End Latent Fingerprint Search Kai Cao Dinh-Luan Nguyen Cori Tymoszek Anil K. Jain 57 24 0 26 Dec 2018
A Multiversion Programming Inspired Approach to Detecting Audio Adversarial Examples Qiang Zeng Jianhai Su Chenglong Fu Golam Kayas Lannan Luo AAML 55 46 0 26 Dec 2018
A Data-driven Adversarial Examples Recognition Framework via Adversarial Feature Genome Li Chen Qi Li Jiawei Zhu Jian Peng Haifeng Li AAML 57 3 0 25 Dec 2018
PPD: Permutation Phase Defense Against Adversarial Examples in Deep Learning Mehdi Jafarnia-Jahromi Tasmin Chowdhury Hsin-Tai Wu S. Mukherjee AAML 47 4 0 25 Dec 2018
DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense Hang Zhou Kejiang Chen Weiming Zhang Han Fang Wenbo Zhou Nenghai Yu 3DPC 69 8 0 25 Dec 2018
Towards resilient machine learning for ransomware detection Li-Wei Chen Chih-Yuan Yang Anindya Paul R. Sahita AAML 36 22 0 21 Dec 2018
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability Xiaowei Huang Daniel Kroening Wenjie Ruan Marta Kwiatkowska Youcheng Sun Emese Thamo Min Wu Xinping Yi AAML 132 51 0 18 Dec 2018
Spartan Networks: Self-Feature-Squeezing Neural Networks for increased robustness in adversarial settings François Menet Paul Berthier José M. Fernandez M. Gagnon AAML 27 10 0 17 Dec 2018
Designing Adversarially Resilient Classifiers using Resilient Feature Engineering Kevin Eykholt A. Prakash AAML 60 4 0 17 Dec 2018
Defense-VAE: A Fast and Accurate Defense against Adversarial Attacks Xiang Li Shihao Ji AAML 75 26 0 17 Dec 2018
Trust Region Based Adversarial Attack on Neural Networks Z. Yao A. Gholami Peng Xu Kurt Keutzer Michael W. Mahoney AAML 64 54 0 16 Dec 2018
Perturbation Analysis of Learning Algorithms: A Unifying Perspective on Generation of Adversarial Examples E. Balda Arash Behboodi R. Mathar AAML 30 5 0 15 Dec 2018
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing Jingyi Wang Guoliang Dong Jun Sun Xinyu Wang Peixin Zhang AAML 78 191 0 14 Dec 2018
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem Matthias Hein Maksym Andriushchenko Julian Bitterwolf OODD 187 559 0 13 Dec 2018
TextBugger: Generating Adversarial Text Against Real-world Applications Jinfeng Li S. Ji Tianyu Du Bo Li Ting Wang SILM AAML 222 750 0 13 Dec 2018
Thwarting Adversarial Examples: An $L_0$ -RobustSparse Fourier Transform Mitali Bafna Jack Murtagh Nikhil Vyas AAML 69 48 0 12 Dec 2018
Adversarial Framing for Image and Video Classification Konrad Zolna Michal Zajac Negar Rostamzadeh Pedro H. O. Pinheiro AAML 106 61 0 11 Dec 2018
On the Security of Randomized Defenses Against Adversarial Samples K. Sharad G. Marson H. Truong Ghassan O. Karame AAML 47 1 0 11 Dec 2018
Defending Against Universal Perturbations With Shared Adversarial Training Chaithanya Kumar Mummadi Thomas Brox J. H. Metzen AAML 84 60 0 10 Dec 2018
Detecting Adversarial Examples in Convolutional Neural Networks Stefanos Pertigkiozoglou Petros Maragos GAN AAML 75 16 0 08 Dec 2018