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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"

50 / 2,298 papers shown

Title
Unravelling Robustness of Deep Learning based Face Recognition Against Adversarial Attacks Gaurav Goswami Nalini Ratha Akshay Agarwal Richa Singh Mayank Vatsa AAML 97 166 0 22 Feb 2018
Generalizable Adversarial Examples Detection Based on Bi-model Decision Mismatch João Monteiro Isabela Albuquerque Zahid Akhtar T. Falk AAML 90 29 0 21 Feb 2018
Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections Xin Zhang Armando Solar-Lezama Rishabh Singh FAtt 115 63 0 21 Feb 2018
On Lyapunov exponents and adversarial perturbation Vinay Uday Prabhu Nishant Desai John Whaley AAML 22 4 0 20 Feb 2018
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression Nilaksh Das Madhuri Shanbhogue Shang-Tse Chen Fred Hohman Siwei Li Li-Wei Chen Michael E. Kounavis Duen Horng Chau FedML AAML 85 228 0 19 Feb 2018
Divide, Denoise, and Defend against Adversarial Attacks Seyed-Mohsen Moosavi-Dezfooli A. Shrivastava Oncel Tuzel AAML 57 45 0 19 Feb 2018
Robustness of Rotation-Equivariant Networks to Adversarial Perturbations Beranger Dumont Simona Maggio Pablo Montalvo AAML 69 24 0 19 Feb 2018
DARTS: Deceiving Autonomous Cars with Toxic Signs Chawin Sitawarin A. Bhagoji Arsalan Mosenia M. Chiang Prateek Mittal AAML 117 236 0 18 Feb 2018
ASP:A Fast Adversarial Attack Example Generation Framework based on Adversarial Saliency Prediction Fuxun Yu Qide Dong Xiang Chen AAML 62 6 0 15 Feb 2018
Learning Privacy Preserving Encodings through Adversarial Training Francesco Pittaluga S. Koppal Ayan Chakrabarti PICV 160 76 0 14 Feb 2018
Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models Mengying Sun Fengyi Tang Jinfeng Yi Fei Wang Jiayu Zhou AAML OOD MedIm 85 63 0 13 Feb 2018
Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples Felix Kreuk A. Barak Shir Aviv-Reuven Moran Baruch Benny Pinkas Joseph Keshet AAML 75 118 0 13 Feb 2018
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks Yusuke Tsuzuku Issei Sato Masashi Sugiyama AAML 115 309 0 12 Feb 2018
Fibres of Failure: Classifying errors in predictive processes L. Carlsson Gunnar Carlsson Mikael Vejdemo-Johansson AI4CE 107 4 0 09 Feb 2018
Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples Adnan Siraj Rakin Zhezhi He Boqing Gong Deliang Fan AAML 87 4 0 05 Feb 2018
First-order Adversarial Vulnerability of Neural Networks and Input Dimension Carl-Johann Simon-Gabriel Yann Ollivier Léon Bottou Bernhard Schölkopf David Lopez-Paz AAML 111 48 0 05 Feb 2018
Towards an Understanding of Neural Networks in Natural-Image Spaces Yifei Fan A. Yezzi AAML GAN 30 2 0 27 Jan 2018
Deflecting Adversarial Attacks with Pixel Deflection Aaditya (Adi) Prakash N. Moran Solomon Garber Antonella DiLillo J. Storer AAML 110 304 0 26 Jan 2018
Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations Konda Reddy Mopuri Aditya Ganeshan R. Venkatesh Babu AAML 151 206 0 24 Jan 2018
Adversarial Texts with Gradient Methods Zhitao Gong Wenlu Wang Yangqiu Song Basel Alomair Wei-Shinn Ku AAML 106 77 0 22 Jan 2018
Sparsity-based Defense against Adversarial Attacks on Linear Classifiers Zhinus Marzi S. Gopalakrishnan Upamanyu Madhow Ramtin Pedarsani AAML 102 31 0 15 Jan 2018
A3T: Adversarially Augmented Adversarial Training Akram Erraqabi A. Baratin Yoshua Bengio Simon Lacoste-Julien AAML 94 9 0 12 Jan 2018
Fooling End-to-end Speaker Verification by Adversarial Examples Felix Kreuk Yossi Adi Moustapha Cissé Joseph Keshet AAML 84 203 0 10 Jan 2018
Less is More: Culling the Training Set to Improve Robustness of Deep Neural Networks Yongshuai Liu Jiyu Chen Hao Chen AAML 82 14 0 09 Jan 2018
Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos Chawin Sitawarin A. Bhagoji Arsalan Mosenia Prateek Mittal M. Chiang AAML 80 69 0 09 Jan 2018
Spatially Transformed Adversarial Examples Chaowei Xiao Jun-Yan Zhu Yue Liu Warren He M. Liu Basel Alomair AAML 104 524 0 08 Jan 2018
Generating Adversarial Examples with Adversarial Networks Chaowei Xiao Yue Liu Jun-Yan Zhu Warren He M. Liu Basel Alomair GAN AAML 129 905 0 08 Jan 2018
Denoising Dictionary Learning Against Adversarial Perturbations John Mitro D. Bridge Steven D. Prestwich AAML 34 5 0 07 Jan 2018
Adversarial Perturbation Intensity Achieving Chosen Intra-Technique Transferability Level for Logistic Regression Martin Gubri AAML 20 0 0 06 Jan 2018
Neural Networks in Adversarial Setting and Ill-Conditioned Weight Space M. Singh Abhishek Sinha Balaji Krishnamurthy AAML 43 7 0 03 Jan 2018
Did you hear that? Adversarial Examples Against Automatic Speech Recognition M. Alzantot Bharathan Balaji Mani B. Srivastava AAML 80 252 0 02 Jan 2018
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey Naveed Akhtar Ajmal Mian AAML 146 1,873 0 02 Jan 2018
A General Framework for Adversarial Examples with Objectives Mahmood Sharif Sruti Bhagavatula Lujo Bauer Michael K. Reiter AAML GAN 84 196 0 31 Dec 2017
Adversarial Patch Tom B. Brown Dandelion Mané Aurko Roy Martín Abadi Justin Gilmer AAML 98 1,099 0 27 Dec 2017
Exploring the Space of Black-box Attacks on Deep Neural Networks A. Bhagoji Warren He Yue Liu Basel Alomair AAML 28 70 0 27 Dec 2017
Using LIP to Gloss Over Faces in Single-Stage Face Detection Networks Siqi Yang Arnold Wiliem Shaokang Chen Brian C. Lovell CVBM AAML 61 3 0 22 Dec 2017
ReabsNet: Detecting and Revising Adversarial Examples Jiefeng Chen Zihang Meng Changtian Sun Weiliang Tang Yinglun Zhu AAML GAN 49 4 0 21 Dec 2017
Adversarial Examples: Attacks and Defenses for Deep Learning Xiaoyong Yuan Pan He Qile Zhu Xiaolin Li SILM AAML 153 1,628 0 19 Dec 2017
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models Wieland Brendel Jonas Rauber Matthias Bethge AAML 103 1,352 0 12 Dec 2017
Training Ensembles to Detect Adversarial Examples Alexander Bagnall Razvan Bunescu Gordon Stewart AAML 57 39 0 11 Dec 2017
NAG: Network for Adversary Generation Konda Reddy Mopuri Utkarsh Ojha Utsav Garg R. Venkatesh Babu AAML 88 146 0 09 Dec 2017
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning Battista Biggio Fabio Roli AAML 181 1,411 0 08 Dec 2017
Exploring the Landscape of Spatial Robustness Logan Engstrom Brandon Tran Dimitris Tsipras Ludwig Schmidt Aleksander Madry AAML 160 363 0 07 Dec 2017
Adversarial Examples that Fool Detectors Jiajun Lu Hussein Sibai Evan Fabry AAML 84 144 0 07 Dec 2017
Generative Adversarial Perturbations Omid Poursaeed Isay Katsman Bicheng Gao Serge J. Belongie AAML GAN WIGM 88 356 0 06 Dec 2017
Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning Hongge Chen Huan Zhang Pin-Yu Chen Jinfeng Yi Cho-Jui Hsieh GAN AAML 84 49 0 06 Dec 2017
A+D Net: Training a Shadow Detector with Adversarial Shadow Attenuation Hieu M. Le T. F. Y. Vicente Vu Nguyen Minh Hoai Dimitris Samaras 49 114 0 04 Dec 2017
Improving Network Robustness against Adversarial Attacks with Compact Convolution Rajeev Ranjan S. Sankaranarayanan Carlos D. Castillo Rama Chellappa AAML 62 14 0 03 Dec 2017
Where Classification Fails, Interpretation Rises Chanh Nguyen Georgi Georgiev Yujie Ji Ting Wang AAML 25 0 0 02 Dec 2017
Measuring the tendency of CNNs to Learn Surface Statistical Regularities Jason Jo Yoshua Bengio AAML 89 250 0 30 Nov 2017