v1v2v3v4 (latest)

Adversarial examples in the physical world

8 July 2016

Papers citing "Adversarial examples in the physical world"

50 / 2,769 papers shown

Title
Adversarial Distillation for Ordered Top-k Attacks Zekun Zhang Tianfu Wu AAML 44 2 0 25 May 2019
Trust but Verify: An Information-Theoretic Explanation for the Adversarial Fragility of Machine Learning Systems, and a General Defense against Adversarial Attacks Jirong Yi Hui Xie Leixin Zhou Xiaodong Wu Weiyu Xu R. Mudumbai AAML 75 6 0 25 May 2019
Thwarting finite difference adversarial attacks with output randomization Haidar Khan Daniel Park Azer Khan B. Yener SILM AAML 52 0 0 23 May 2019
Interpreting Adversarially Trained Convolutional Neural Networks Tianyuan Zhang Zhanxing Zhu AAML GAN FAtt 125 161 0 23 May 2019
Biometric Backdoors: A Poisoning Attack Against Unsupervised Template Updating Giulio Lovisotto Simon Eberz Ivan Martinovic AAML 88 36 0 22 May 2019
DoPa: A Comprehensive CNN Detection Methodology against Physical Adversarial Attacks Zirui Xu Fuxun Yu Xiang Chen AAML 45 0 0 21 May 2019
Testing DNN Image Classifiers for Confusion & Bias Errors Yuchi Tian Ziyuan Zhong Vicente Ordonez Gail E. Kaiser Baishakhi Ray 153 53 0 20 May 2019
Predicting Model Failure using Saliency Maps in Autonomous Driving Systems Sina Mohseni Akshay V. Jagadeesh Zhangyang Wang 78 14 0 19 May 2019
Taking Care of The Discretization Problem: A Comprehensive Study of the Discretization Problem and A Black-Box Adversarial Attack in Discrete Integer Domain Lei Bu Yuchao Duan Fu Song Zhe Zhao AAML 114 18 0 19 May 2019
What Do Adversarially Robust Models Look At? Takahiro Itazuri Yoshihiro Fukuhara Hirokatsu Kataoka Shigeo Morishima 32 5 0 19 May 2019
POPQORN: Quantifying Robustness of Recurrent Neural Networks Ching-Yun Ko Zhaoyang Lyu Tsui-Wei Weng Luca Daniel Ngai Wong Dahua Lin AAML 69 76 0 17 May 2019
A critique of the DeepSec Platform for Security Analysis of Deep Learning Models Nicholas Carlini ELM 68 14 0 17 May 2019
An Efficient Pre-processing Method to Eliminate Adversarial Effects Hua Wang Jie Wang Z. Yin AAML 38 1 0 15 May 2019
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets Chen Zhu Wenjie Huang Ali Shafahi Hengduo Li Gavin Taylor Christoph Studer Tom Goldstein 125 286 0 15 May 2019
Moving Target Defense for Deep Visual Sensing against Adversarial Examples Qun Song Zhenyu Yan Rui Tan AAML 47 21 0 11 May 2019
Interpreting and Evaluating Neural Network Robustness Fuxun Yu Zhuwei Qin Chenchen Liu Liang Zhao Yanzhi Wang Xiang Chen AAML 57 56 0 10 May 2019
On the Connection Between Adversarial Robustness and Saliency Map Interpretability Christian Etmann Sebastian Lunz Peter Maass Carola-Bibiane Schönlieb AAML FAtt 63 162 0 10 May 2019
Exact Adversarial Attack to Image Captioning via Structured Output Learning with Latent Variables Yan Xu Baoyuan Wu Fumin Shen Yanbo Fan Yong Zhang Heng Tao Shen Wei Liu AAML 78 56 0 10 May 2019
Exploring the Hyperparameter Landscape of Adversarial Robustness Evelyn Duesterwald Anupama Murthi Ganesh Venkataraman M. Sinn Deepak Vijaykeerthy AAML 52 7 0 09 May 2019
Universal Adversarial Perturbations for Speech Recognition Systems Paarth Neekhara Shehzeen Samarah Hussain Prakhar Pandey Shlomo Dubnov Julian McAuley F. Koushanfar AAML 82 118 0 09 May 2019
AI Enabling Technologies: A Survey V. Gadepally Justin A. Goodwin J. Kepner Albert Reuther Hayley Reynolds S. Samsi Jonathan Su David Martinez 46 25 0 08 May 2019
A Comprehensive Analysis on Adversarial Robustness of Spiking Neural Networks Saima Sharmin Priyadarshini Panda Syed Shakib Sarwar Chankyu Lee Wachirawit Ponghiran Kaushik Roy AAML 57 67 0 07 May 2019
Representation of White- and Black-Box Adversarial Examples in Deep Neural Networks and Humans: A Functional Magnetic Resonance Imaging Study Chihye Han Wonjun Yoon Gihyun Kwon S. Nam Dae-Shik Kim AAML 53 5 0 07 May 2019
Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples Vikash Sehwag A. Bhagoji Liwei Song Chawin Sitawarin Daniel Cullina M. Chiang Prateek Mittal OODD 79 26 0 05 May 2019
When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing Z. Luo Shangqing Zhao Zhuo Lu Jie Xu Y. Sagduyu AAML 89 53 0 04 May 2019
Adversarial Training with Voronoi Constraints Marc Khoury Dylan Hadfield-Menell AAML 63 24 0 02 May 2019
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks Yandong Li Lijun Li Liqiang Wang Tong Zhang Boqing Gong AAML 86 245 0 01 May 2019
POBA-GA: Perturbation Optimized Black-Box Adversarial Attacks via Genetic Algorithm Jinyin Chen Mengmeng Su Shijing Shen Hui Xiong Haibin Zheng AAML 124 68 0 01 May 2019
Test Selection for Deep Learning Systems Wei Ma Mike Papadakis Anestis Tsakmalis Maxime Cordy Yves Le Traon OOD 73 93 0 30 Apr 2019
Detecting Adversarial Examples through Nonlinear Dimensionality Reduction Francesco Crecchi D. Bacciu Battista Biggio AAML 83 10 0 30 Apr 2019
Adversarial Training for Free! Ali Shafahi Mahyar Najibi Amin Ghiasi Zheng Xu John P. Dickerson Christoph Studer L. Davis Gavin Taylor Tom Goldstein AAML 139 1,255 0 29 Apr 2019
Data Poisoning Attack against Knowledge Graph Embedding Hengtong Zhang T. Zheng Jing Gao Chenglin Miao Lu Su Yaliang Li K. Ren KELM 71 84 0 26 Apr 2019
General risk measures for robust machine learning Émilie Chouzenoux Henri Gérard J. Pesquet OOD 50 7 0 26 Apr 2019
Physical Adversarial Textures that Fool Visual Object Tracking R. Wiyatno Anqi Xu AAML 95 74 0 24 Apr 2019
A Robust Approach for Securing Audio Classification Against Adversarial Attacks Mohammad Esmaeilpour P. Cardinal Alessandro Lameiras Koerich AAML 74 71 0 24 Apr 2019
Minimizing Perceived Image Quality Loss Through Adversarial Attack Scoping K. Khabarlak L. Koriashkina AAML 39 1 0 23 Apr 2019
Using Videos to Evaluate Image Model Robustness Keren Gu Brandon Yang Jiquan Ngiam Quoc V. Le Jonathon Shlens AAML 79 44 0 22 Apr 2019
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach R. Taheri R. Javidan Mohammad Shojafar P. Vinod Mauro Conti AAML 49 34 0 20 Apr 2019
Gotta Catch Ém All: Using Honeypots to Catch Adversarial Attacks on Neural Networks Shawn Shan Emily Wenger Bolun Wang Yangqiu Song Haitao Zheng Ben Y. Zhao 89 75 0 18 Apr 2019
Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers Ameya Joshi Amitangshu Mukherjee Soumik Sarkar Chinmay Hegde AAML 92 100 0 17 Apr 2019
Interpreting Adversarial Examples with Attributes Sadaf Gulshad J. H. Metzen A. Smeulders Zeynep Akata FAtt AAML 93 6 0 17 Apr 2019
Adversarial Defense Through Network Profiling Based Path Extraction Yuxian Qiu Jingwen Leng Cong Guo Quan Chen Chong Li Minyi Guo Yuhao Zhu AAML 69 51 0 17 Apr 2019
Reducing Adversarial Example Transferability Using Gradient Regularization George Adam P. Smirnov B. Haibe-Kains Anna Goldenberg AAML 81 4 0 16 Apr 2019
Are Nearby Neighbors Relatives?: Testing Deep Music Embeddings Jaehun Kim Julián Urbano Cynthia C. S. Liem Alan Hanjalic 40 0 0 15 Apr 2019
Unrestricted Adversarial Examples via Semantic Manipulation Anand Bhattad Min Jin Chong Kaizhao Liang Yangqiu Song David A. Forsyth AAML 85 153 0 12 Apr 2019
Generating Minimal Adversarial Perturbations with Integrated Adaptive Gradients Yatie Xiao Chi-Man Pun AAML GAN TTA 21 0 0 12 Apr 2019
Cycle-Consistent Adversarial GAN: the integration of adversarial attack and defense Lingyun Jiang Kai Qiao Ruoxi Qin Linyuan Wang Jian Chen Haibing Bu Bin Yan AAML 35 8 0 12 Apr 2019
Deep learning as optimal control problems: models and numerical methods Martin Benning E. Celledoni Matthias Joachim Ehrhardt B. Owren Carola-Bibiane Schönlieb 137 81 0 11 Apr 2019
StegaStamp: Invisible Hyperlinks in Physical Photographs Matthew Tancik B. Mildenhall Ren Ng WIGM 121 378 0 10 Apr 2019
Black-box Adversarial Attacks on Video Recognition Models Linxi Jiang Xingjun Ma Shaoxiang Chen James Bailey Yu-Gang Jiang AAML MLAU 76 149 0 10 Apr 2019