Towards Deep Learning Models Resistant to Adversarial Attacks

19 June 2017

Papers citing "Towards Deep Learning Models Resistant to Adversarial Attacks"

50 / 6,519 papers shown

Title
Variational Inference with Latent Space Quantization for Adversarial Resilience Vinay Kyatham P. PrathoshA. Tarun Kumar Yadav Deepak Mishra Dheeraj Mundhra AAML 19 3 0 24 Mar 2019
Scalable Differential Privacy with Certified Robustness in Adversarial Learning Nhathai Phan My T. Thai Han Hu R. Jin Tong Sun Dejing Dou 37 14 0 23 Mar 2019
Improving Adversarial Robustness via Guided Complement Entropy Hao-Yun Chen Jhao-Hong Liang Shih-Chieh Chang Jia Pan Yu-Ting Chen Wei Wei Da-Cheng Juan AAML 14 47 0 23 Mar 2019
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition Yao Qin Nicholas Carlini Ian Goodfellow G. Cottrell Colin Raffel AAML 38 377 0 22 Mar 2019
Adversarial camera stickers: A physical camera-based attack on deep learning systems Juncheng Billy Li Frank R. Schmidt J. Zico Kolter AAML 16 164 0 21 Mar 2019
Interpreting Neural Networks Using Flip Points Roozbeh Yousefzadeh D. O’Leary AAML FAtt 22 17 0 21 Mar 2019
Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes Matt Jordan Justin Lewis A. Dimakis AAML 29 57 0 20 Mar 2019
Implicit Generation and Generalization in Energy-Based Models Yilun Du Igor Mordatch BDL DiffM 19 40 0 20 Mar 2019
On the Robustness of Deep K-Nearest Neighbors Chawin Sitawarin David Wagner AAML OOD 11 58 0 20 Mar 2019
On Certifying Non-uniform Bound against Adversarial Attacks Chen Liu Ryota Tomioka V. Cevher AAML 50 19 0 15 Mar 2019
A Research Agenda: Dynamic Models to Defend Against Correlated Attacks Ian Goodfellow AAML OOD 43 31 0 14 Mar 2019
Attribution-driven Causal Analysis for Detection of Adversarial Examples Susmit Jha Sunny Raj S. Fernandes Sumit Kumar Jha S. Jha Gunjan Verma B. Jalaeian A. Swami AAML 25 17 0 14 Mar 2019
Semantics Preserving Adversarial Learning Ousmane Amadou Dia Elnaz Barshan Reza Babanezhad AAML GAN 36 2 0 10 Mar 2019
GanDef: A GAN based Adversarial Training Defense for Neural Network Classifier Guanxiong Liu Issa M. Khalil Abdallah Khreishah GAN AAML 33 19 0 06 Mar 2019
Detecting Overfitting via Adversarial Examples Roman Werpachowski András Gyorgy Csaba Szepesvári TDI 26 45 0 06 Mar 2019
Negative Training for Neural Dialogue Response Generation Tianxing He James R. Glass 30 59 0 06 Mar 2019
Statistical Guarantees for the Robustness of Bayesian Neural Networks L. Cardelli Marta Kwiatkowska Luca Laurenti Nicola Paoletti A. Patané Matthew Wicker AAML 31 54 0 05 Mar 2019
Defense Against Adversarial Images using Web-Scale Nearest-Neighbor Search Abhimanyu Dubey Laurens van der Maaten Zeki Yalniz Yixuan Li D. Mahajan AAML 33 64 0 05 Mar 2019
A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations Saeid Asgari Taghanaki Kumar Abhishek Shekoofeh Azizi Ghassan Hamarneh AAML 31 40 0 03 Mar 2019
PuVAE: A Variational Autoencoder to Purify Adversarial Examples Uiwon Hwang Jaewoo Park Hyemi Jang Sungroh Yoon N. Cho AAML 20 76 0 02 Mar 2019
On the Effectiveness of Low Frequency Perturbations Yash Sharma G. Ding Marcus A. Brubaker AAML 38 121 0 28 Feb 2019
Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN Ke Sun Zhanxing Zhu Zhouchen Lin AAML 27 20 0 28 Feb 2019
Towards Understanding Adversarial Examples Systematically: Exploring Data Size, Task and Model Factors Ke Sun Zhanxing Zhu Zhouchen Lin AAML 30 18 0 28 Feb 2019
Adversarial Attack and Defense on Point Sets Jiancheng Yang Qiang Zhang Rongyao Fang Bingbing Ni Jinxian Liu Qi Tian 3DPC 24 122 0 28 Feb 2019
Adversarial Attacks on Time Series Fazle Karim Somshubra Majumdar H. Darabi AI4TS 23 97 0 27 Feb 2019
The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detection Muhammad Zaid Hameed András Gyorgy Deniz Gunduz AAML 21 72 0 27 Feb 2019
Robust Decision Trees Against Adversarial Examples Hongge Chen Huan Zhang Duane S. Boning Cho-Jui Hsieh AAML 31 116 0 27 Feb 2019
Verification of Non-Linear Specifications for Neural Networks Chongli Qin Krishnamurthy Dvijotham Dvijotham Brendan O'Donoghue Rudy Bunel Robert Stanforth Sven Gowal J. Uesato G. Swirszcz Pushmeet Kohli AAML 19 43 0 25 Feb 2019
Adversarial attacks hidden in plain sight Jan Philip Göpfert André Artelt H. Wersing Barbara Hammer AAML 25 17 0 25 Feb 2019
Adversarial Reinforcement Learning under Partial Observability in Autonomous Computer Network Defence Yi Han David Hubczenko Paul Montague O. Vel Tamas Abraham Benjamin I. P. Rubinstein C. Leckie T. Alpcan S. Erfani AAML 16 6 0 25 Feb 2019
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks Hadi Salman Greg Yang Huan Zhang Cho-Jui Hsieh Pengchuan Zhang AAML 52 263 0 23 Feb 2019
On the Sensitivity of Adversarial Robustness to Input Data Distributions G. Ding Kry Yik-Chau Lui Xiaomeng Jin Luyu Wang Ruitong Huang OOD 26 59 0 22 Feb 2019
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods Maher Nouiehed Maziar Sanjabi Tianjian Huang Jason D. Lee Meisam Razaviyayn 48 338 0 21 Feb 2019
Quantifying Perceptual Distortion of Adversarial Examples Matt Jordan N. Manoj Surbhi Goel A. Dimakis 19 39 0 21 Feb 2019
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations Eric Wong Frank R. Schmidt J. Zico Kolter AAML 36 210 0 21 Feb 2019
Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers Diego Gragnaniello Francesco Marra Giovanni Poggi L. Verdoliva AAML 19 30 0 20 Feb 2019
advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch G. Ding Luyu Wang Xiaomeng Jin 24 182 0 20 Feb 2019
Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure Fuli Feng Xiangnan He Jie Tang Tat-Seng Chua OOD AAML 34 219 0 20 Feb 2019
There are No Bit Parts for Sign Bits in Black-Box Attacks Abdullah Al-Dujaili Una-May O’Reilly AAML 21 20 0 19 Feb 2019
On Evaluating Adversarial Robustness Nicholas Carlini Anish Athalye Nicolas Papernot Wieland Brendel Jonas Rauber Dimitris Tsipras Ian Goodfellow Aleksander Madry Alexey Kurakin ELM AAML 37 892 0 18 Feb 2019
Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks with Adversarial Traces Mohammad Saidur Rahman Mohsen Imani Nate Mathews M. Wright AAML 14 80 0 18 Feb 2019
AuxBlocks: Defense Adversarial Example via Auxiliary Blocks Yueyao Yu Pengfei Yu Wenye Li AAML 14 6 0 18 Feb 2019
Mitigation of Adversarial Examples in RF Deep Classifiers Utilizing AutoEncoder Pre-training S. Kokalj-Filipovic Rob Miller Nicholas Chang Chi Leung Lau AAML 22 36 0 16 Feb 2019
Adversarial Examples in RF Deep Learning: Detection of the Attack and its Physical Robustness S. Kokalj-Filipovic Rob Miller AAML 25 31 0 16 Feb 2019
Do ImageNet Classifiers Generalize to ImageNet? Benjamin Recht Rebecca Roelofs Ludwig Schmidt Vaishaal Shankar OOD SSeg VLM 40 1,665 0 13 Feb 2019
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples Kevin Roth Yannic Kilcher Thomas Hofmann AAML 27 175 0 13 Feb 2019
Examining Adversarial Learning against Graph-based IoT Malware Detection Systems Ahmed A. Abusnaina Aminollah Khormali Hisham Alasmary Jeman Park Afsah Anwar Ulku Meteriz Aziz Mohaisen AAML 18 5 0 12 Feb 2019
Towards a Robust Deep Neural Network in Texts: A Survey Wenqi Wang Benxiao Tang Run Wang Lina Wang Aoshuang Ye AAML 26 39 0 12 Feb 2019
VC Classes are Adversarially Robustly Learnable, but Only Improperly Omar Montasser Steve Hanneke Nathan Srebro 12 138 0 12 Feb 2019
Model Compression with Adversarial Robustness: A Unified Optimization Framework Shupeng Gui Haotao Wang Chen Yu Haichuan Yang Zhangyang Wang Ji Liu MQ 19 137 0 10 Feb 2019