Adversarial examples in the physical world

8 July 2016

Papers citing "Adversarial examples in the physical world"

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Title
Adversarial Training is a Form of Data-dependent Operator Norm Regularization Kevin Roth Yannic Kilcher Thomas Hofmann 17 13 0 04 Jun 2019
Interpretable Neural Network Decoupling Yuchao Li Rongrong Ji Shaohui Lin Baochang Zhang Chenqian Yan Yongjian Wu Feiyue Huang Ling Shao 37 2 0 04 Jun 2019
A Surprising Density of Illusionable Natural Speech M. Guan Gregory Valiant AAML 19 3 0 03 Jun 2019
Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness Nhathai Phan Minh Nhat Vu Yang Liu R. Jin Dejing Dou Xintao Wu My T. Thai AAML 22 51 0 02 Jun 2019
Adversarial Examples for Edge Detection: They Exist, and They Transfer Christian Cosgrove Alan Yuille AAML GAN 25 12 0 02 Jun 2019
Enhancing Transformation-based Defenses using a Distribution Classifier C. Kou H. Lee E. Chang Teck Khim Ng 37 3 0 01 Jun 2019
Perceptual Evaluation of Adversarial Attacks for CNN-based Image Classification Sid Ahmed Fezza Yassine Bakhti W. Hamidouche Olivier Déforges AAML 14 31 0 01 Jun 2019
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness A. Malinin Mark Gales UQCV AAML 27 172 0 31 May 2019
Residual Networks as Nonlinear Systems: Stability Analysis using Linearization Kai Rothauge Z. Yao Zixi Hu Michael W. Mahoney 13 2 0 31 May 2019
Interpretable Adversarial Training for Text Samuel Barham S. Feizi AAML 21 17 0 30 May 2019
Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward A. Qayyum Muhammad Usama Junaid Qadir Ala I. Al-Fuqaha AAML 27 187 0 29 May 2019
CopyCAT: Taking Control of Neural Policies with Constant Attacks Léonard Hussenot M. Geist Olivier Pietquin AAML 17 30 0 29 May 2019
An Investigation of Data Poisoning Defenses for Online Learning Yizhen Wang Somesh Jha Kamalika Chaudhuri AAML 13 5 0 28 May 2019
Certifiably Robust Interpretation in Deep Learning Alexander Levine Sahil Singla S. Feizi FAtt AAML 31 63 0 28 May 2019
High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks Haohan Wang Xindi Wu Pengcheng Yin Eric Xing 22 513 0 28 May 2019
Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss Pengcheng Li Jinfeng Yi Bowen Zhou Lijun Zhang AAML 37 36 0 28 May 2019
Label Universal Targeted Attack Naveed Akhtar M. Jalwana Bennamoun Ajmal Mian AAML 16 5 0 27 May 2019
GAT: Generative Adversarial Training for Adversarial Example Detection and Robust Classification Xuwang Yin Soheil Kolouri Gustavo K. Rohde AAML 30 43 0 27 May 2019
Scaleable input gradient regularization for adversarial robustness Chris Finlay Adam M. Oberman AAML 16 77 0 27 May 2019
$Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$$ Provable robustness against all adversarial $l_p$ -perturbations for $p\geq 1$ Francesco Croce Matthias Hein OOD 17 75 0 27 May 2019
Non-Determinism in Neural Networks for Adversarial Robustness Daanish Ali Khan Linhong Li Ninghao Sha Zhuoran Liu Abelino Jiménez Bhiksha Raj Rita Singh OOD AAML 11 3 0 26 May 2019
Rearchitecting Classification Frameworks For Increased Robustness Varun Chandrasekaran Brian Tang Nicolas Papernot Kassem Fawaz S. Jha Xi Wu AAML OOD 42 8 0 26 May 2019
Adversarial Distillation for Ordered Top-k Attacks Zekun Zhang Tianfu Wu AAML 6 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 22 6 0 25 May 2019
Thwarting finite difference adversarial attacks with output randomization Haidar Khan Daniel Park Azer Khan B. Yener SILM AAML 41 0 0 23 May 2019
Interpreting Adversarially Trained Convolutional Neural Networks Tianyuan Zhang Zhanxing Zhu AAML GAN FAtt 28 158 0 23 May 2019
Biometric Backdoors: A Poisoning Attack Against Unsupervised Template Updating Giulio Lovisotto Simon Eberz Ivan Martinovic AAML 21 35 0 22 May 2019
DoPa: A Comprehensive CNN Detection Methodology against Physical Adversarial Attacks Zirui Xu Fuxun Yu Xiang Chen AAML 11 0 0 21 May 2019
Testing DNN Image Classifiers for Confusion & Bias Errors Yuchi Tian Ziyuan Zhong Vicente Ordonez Gail E. Kaiser Baishakhi Ray 24 52 0 20 May 2019
Predicting Model Failure using Saliency Maps in Autonomous Driving Systems Sina Mohseni Akshay V. Jagadeesh Zhangyang Wang 27 13 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 37 18 0 19 May 2019
What Do Adversarially Robust Models Look At? Takahiro Itazuri Yoshihiro Fukuhara Hirokatsu Kataoka Shigeo Morishima 19 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 17 75 0 17 May 2019
A critique of the DeepSec Platform for Security Analysis of Deep Learning Models Nicholas Carlini ELM 17 14 0 17 May 2019
An Efficient Pre-processing Method to Eliminate Adversarial Effects Hua Wang Jie Wang Z. Yin AAML 14 1 0 15 May 2019
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets Chen Zhu Yifan Jiang Ali Shafahi Hengduo Li Gavin Taylor Christoph Studer Tom Goldstein 29 283 0 15 May 2019
Moving Target Defense for Deep Visual Sensing against Adversarial Examples Qun Song Zhenyu Yan Rui Tan AAML 21 20 0 11 May 2019
Interpreting and Evaluating Neural Network Robustness Fuxun Yu Zhuwei Qin Chenchen Liu Liang Zhao Yanzhi Wang Xiang Chen AAML 15 55 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 23 157 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 25 55 0 10 May 2019
Exploring the Hyperparameter Landscape of Adversarial Robustness Evelyn Duesterwald Anupama Murthi Ganesh Venkataraman M. Sinn Deepak Vijaykeerthy AAML 16 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 36 113 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 27 24 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 24 66 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 18 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 32 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 25 53 0 04 May 2019
Adversarial Training with Voronoi Constraints Marc Khoury Dylan Hadfield-Menell AAML 20 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 18 245 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 22 67 0 01 May 2019