Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods

20 May 2017

Papers citing "Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods"

49 / 349 papers shown

Title
Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models Tong Niu Joey Tianyi Zhou AAML 21 85 0 06 Sep 2018
DeepHunter: Hunting Deep Neural Network Defects via Coverage-Guided Fuzzing Xiaofei Xie L. Ma Felix Juefei Xu Hongxu Chen Minhui Xue Bo-wen Li Yang Liu Jianjun Zhao Jianxiong Yin Simon See 40 40 0 04 Sep 2018
Are You Tampering With My Data? Michele Alberti Vinaychandran Pondenkandath Marcel Würsch Manuel Bouillon Mathias Seuret Rolf Ingold Marcus Liwicki AAML 37 19 0 21 Aug 2018
Controlling Over-generalization and its Effect on Adversarial Examples Generation and Detection Mahdieh Abbasi Arezoo Rajabi A. Mozafari R. Bobba Christian Gagné AAML 24 9 0 21 Aug 2018
Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models D. Su Huan Zhang Hongge Chen Jinfeng Yi Pin-Yu Chen Yupeng Gao VLM 35 388 0 05 Aug 2018
Motivating the Rules of the Game for Adversarial Example Research Justin Gilmer Ryan P. Adams Ian Goodfellow David G. Andersen George E. Dahl AAML 50 226 0 18 Jul 2018
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks Kimin Lee Kibok Lee Honglak Lee Jinwoo Shin OODD 23 1,997 0 10 Jul 2018
Adversarial Examples in Deep Learning: Characterization and Divergence Wenqi Wei Ling Liu Margaret Loper Stacey Truex Lei Yu Mehmet Emre Gursoy Yanzhao Wu AAML SILM 33 18 0 29 Jun 2018
Non-Negative Networks Against Adversarial Attacks William Fleshman Edward Raff Jared Sylvester Steven Forsyth Mark McLean AAML 27 41 0 15 Jun 2018
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 40 1,840 0 31 May 2018
AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks Chun-Chen Tu Pai-Shun Ting Pin-Yu Chen Sijia Liu Huan Zhang Jinfeng Yi Cho-Jui Hsieh Shin-Ming Cheng MLAU AAML 20 394 0 30 May 2018
Automated Verification of Neural Networks: Advances, Challenges and Perspectives Francesco Leofante Nina Narodytska Luca Pulina A. Tacchella AAML 12 69 0 25 May 2018
Curriculum Adversarial Training Qi-Zhi Cai Min Du Chang-rui Liu D. Song AAML 24 159 0 13 May 2018
Towards Dependable Deep Convolutional Neural Networks (CNNs) with Out-distribution Learning Mahdieh Abbasi Arezoo Rajabi Christian Gagné R. Bobba OODD 25 6 0 24 Apr 2018
VectorDefense: Vectorization as a Defense to Adversarial Examples V. Kabilan Brandon L. Morris Anh Totti Nguyen AAML 22 21 0 23 Apr 2018
MEADE: Towards a Malicious Email Attachment Detection Engine Ethan M. Rudd Richard E. Harang Joshua Saxe 25 32 0 22 Apr 2018
ADef: an Iterative Algorithm to Construct Adversarial Deformations Rima Alaifari Giovanni S. Alberti Tandri Gauksson AAML 19 96 0 20 Apr 2018
Semantic Adversarial Deep Learning S. Seshia S. Jha T. Dreossi AAML SILM 27 90 0 19 Apr 2018
Understanding Measures of Uncertainty for Adversarial Example Detection Lewis Smith Y. Gal UQCV 52 358 0 22 Mar 2018
Technical Report: When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks Octavian Suciu R. Marginean Yigitcan Kaya Hal Daumé Tudor Dumitras AAML 31 283 0 19 Mar 2018
Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-task Training Derui Wang Chaoran Li S. Wen Surya Nepal Yang Xiang AAML 33 29 0 14 Mar 2018
Robust GANs against Dishonest Adversaries Zhi Xu Chengtao Li Stefanie Jegelka AAML 34 3 0 27 Feb 2018
Adversarial vulnerability for any classifier Alhussein Fawzi Hamza Fawzi Omar Fawzi AAML 27 248 0 23 Feb 2018
Deep Defense: Training DNNs with Improved Adversarial Robustness Ziang Yan Yiwen Guo Changshui Zhang AAML 35 109 0 23 Feb 2018
L2-Nonexpansive Neural Networks Haifeng Qian M. Wegman 25 74 0 22 Feb 2018
Secure Detection of Image Manipulation by means of Random Feature Selection Z. Chen B. Tondi Xiaolong Li R. Ni Yao-Min Zhao Mauro Barni AAML 25 33 0 02 Feb 2018
Adversarial Deep Learning for Robust Detection of Binary Encoded Malware Abdullah Al-Dujaili Alex Huang Erik Hemberg Una-May O’Reilly AAML 25 186 0 09 Jan 2018
Less is More: Culling the Training Set to Improve Robustness of Deep Neural Networks Yongshuai Liu Jiyu Chen Hao Chen AAML 14 14 0 09 Jan 2018
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality Xingjun Ma Bo-wen Li Yisen Wang S. Erfani S. Wijewickrema Grant Schoenebeck D. Song Michael E. Houle James Bailey AAML 26 726 0 08 Jan 2018
A General Framework for Adversarial Examples with Objectives Mahmood Sharif Sruti Bhagavatula Lujo Bauer Michael K. Reiter AAML GAN 13 191 0 31 Dec 2017
The Robust Manifold Defense: Adversarial Training using Generative Models A. Jalal Andrew Ilyas C. Daskalakis A. Dimakis AAML 31 174 0 26 Dec 2017
Training Ensembles to Detect Adversarial Examples Alexander Bagnall Razvan Bunescu Gordon Stewart AAML 10 38 0 11 Dec 2017
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning Battista Biggio Fabio Roli AAML 25 1,388 0 08 Dec 2017
Improving Network Robustness against Adversarial Attacks with Compact Convolution Rajeev Ranjan S. Sankaranarayanan Carlos D. Castillo Rama Chellappa AAML 21 14 0 03 Dec 2017
Towards Robust Neural Networks via Random Self-ensemble Xuanqing Liu Minhao Cheng Huan Zhang Cho-Jui Hsieh FedML AAML 38 418 0 02 Dec 2017
Evaluating Robustness of Neural Networks with Mixed Integer Programming Vincent Tjeng Kai Y. Xiao Russ Tedrake AAML 52 117 0 20 Nov 2017
MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis Rushil Anirudh Jayaraman J. Thiagarajan R. Sridhar T. Bremer FAtt AAML 23 12 0 15 Nov 2017
Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight Yen-Chen Lin Ming-Yu Liu Min Sun Jia-Bin Huang AAML 29 48 0 02 Oct 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 24 636 0 13 Sep 2017
DeepFense: Online Accelerated Defense Against Adversarial Deep Learning B. Rouhani Mohammad Samragh Mojan Javaheripi T. Javidi F. Koushanfar AAML 12 15 0 08 Sep 2017
PassGAN: A Deep Learning Approach for Password Guessing Briland Hitaj Paolo Gasti G. Ateniese Fernando Perez-Cruz GAN 30 246 0 01 Sep 2017
Towards Deep Learning Models Resistant to Adversarial Attacks A. Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 53 11,854 0 19 Jun 2017
Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong Warren He James Wei Xinyun Chen Nicholas Carlini D. Song AAML 29 242 0 15 Jun 2017
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation Matthias Hein Maksym Andriushchenko AAML 45 505 0 23 May 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 65 2,699 0 19 May 2017
On the (Statistical) Detection of Adversarial Examples Kathrin Grosse Praveen Manoharan Nicolas Papernot Michael Backes Patrick McDaniel AAML 23 709 0 21 Feb 2017
Deep Reinforcement Learning: An Overview Yuxi Li OffRL VLM 104 1,503 0 25 Jan 2017
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation Yonghui Wu M. Schuster Z. Chen Quoc V. Le Mohammad Norouzi ... Alex Rudnick Oriol Vinyals G. Corrado Macduff Hughes J. Dean AIMat 716 6,746 0 26 Sep 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 287 5,842 0 08 Jul 2016