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"

50 / 352 papers shown

Title
Anomalous Example Detection in Deep Learning: A Survey Saikiran Bulusu B. Kailkhura Bo-wen Li P. Varshney D. Song AAML 28 47 0 16 Mar 2020
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness Ahmadreza Jeddi M. Shafiee Michelle Karg C. Scharfenberger A. Wong OOD AAML 67 63 0 02 Mar 2020
Overfitting in adversarially robust deep learning Leslie Rice Eric Wong Zico Kolter 47 785 0 26 Feb 2020
Real-Time Detectors for Digital and Physical Adversarial Inputs to Perception Systems Y. Kantaros Taylor J. Carpenter Kaustubh Sridhar Yahan Yang Insup Lee James Weimer AAML 17 12 0 23 Feb 2020
Non-Intrusive Detection of Adversarial Deep Learning Attacks via Observer Networks K. Sivamani R. Sahay Aly El Gamal AAML 11 3 0 22 Feb 2020
On Adaptive Attacks to Adversarial Example Defenses Florian Tramèr Nicholas Carlini Wieland Brendel A. Madry AAML 104 820 0 19 Feb 2020
Deflecting Adversarial Attacks Yao Qin Nicholas Frosst Colin Raffel G. Cottrell Geoffrey E. Hinton AAML 30 15 0 18 Feb 2020
CEB Improves Model Robustness Ian S. Fischer Alexander A. Alemi AAML 19 28 0 13 Feb 2020
The Conditional Entropy Bottleneck Ian S. Fischer OOD 27 115 0 13 Feb 2020
More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Models Lin Chen Yifei Min Mingrui Zhang Amin Karbasi OOD 38 64 0 11 Feb 2020
Robustness of Bayesian Neural Networks to Gradient-Based Attacks Ginevra Carbone Matthew Wicker Luca Laurenti A. Patané Luca Bortolussi G. Sanguinetti AAML 38 77 0 11 Feb 2020
Importance-Driven Deep Learning System Testing Simos Gerasimou Hasan Ferit Eniser A. Sen Alper Çakan AAML VLM 30 98 0 09 Feb 2020
Certified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothing Jinyuan Jia Binghui Wang Xiaoyu Cao Neil Zhenqiang Gong AAML 83 83 0 09 Feb 2020
Analysis of Random Perturbations for Robust Convolutional Neural Networks Adam Dziedzic S. Krishnan OOD AAML 24 1 0 08 Feb 2020
Minimax Defense against Gradient-based Adversarial Attacks Blerta Lindqvist R. Izmailov AAML 19 0 0 04 Feb 2020
Adversarial Machine Learning -- Industry Perspectives Ramnath Kumar Magnus Nyström J. Lambert Andrew Marshall Mario Goertzel Andi Comissoneru Matt Swann Sharon Xia AAML SILM 29 232 0 04 Feb 2020
Defending Adversarial Attacks via Semantic Feature Manipulation Shuo Wang Tianle Chen Surya Nepal Carsten Rudolph M. Grobler Shangyu Chen AAML 24 5 0 03 Feb 2020
Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet Sizhe Chen Zhengbao He Chengjin Sun Jie-jin Yang Xiaolin Huang AAML 31 103 0 16 Jan 2020
Fast is better than free: Revisiting adversarial training Eric Wong Leslie Rice J. Zico Kolter AAML OOD 99 1,158 0 12 Jan 2020
WAF-A-MoLE: Evading Web Application Firewalls through Adversarial Machine Learning Christian Scano Biagio Montaruli Gabriele Costa Giovanni Lagorio AAML 21 27 0 07 Jan 2020
Efficient Adversarial Training with Transferable Adversarial Examples Haizhong Zheng Ziqi Zhang Juncheng Gu Honglak Lee A. Prakash AAML 24 108 0 27 Dec 2019
Benchmarking Adversarial Robustness Yinpeng Dong Qi-An Fu Xiao Yang Tianyu Pang Hang Su Zihao Xiao Jun Zhu AAML 28 36 0 26 Dec 2019
Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes Keane Lucas Mahmood Sharif Lujo Bauer Michael K. Reiter S. Shintre AAML 31 66 0 19 Dec 2019
Deep Neural Network Fingerprinting by Conferrable Adversarial Examples Nils Lukas Yuxuan Zhang Florian Kerschbaum MLAU FedML AAML 31 144 0 02 Dec 2019
Using Depth for Pixel-Wise Detection of Adversarial Attacks in Crowd Counting Weizhe Liu Mathieu Salzmann Pascal Fua AAML 27 9 0 26 Nov 2019
Playing it Safe: Adversarial Robustness with an Abstain Option Cassidy Laidlaw S. Feizi AAML 31 20 0 25 Nov 2019
Fine-grained Synthesis of Unrestricted Adversarial Examples Omid Poursaeed Tianxing Jiang Yordanos Goshu Harry Yang Serge J. Belongie Ser-Nam Lim AAML 37 13 0 20 Nov 2019
Generate (non-software) Bugs to Fool Classifiers Hiromu Yakura Youhei Akimoto Jun Sakuma AAML 22 10 0 20 Nov 2019
Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models Tong Che Xiaofeng Liu Site Li Yubin Ge Ruixiang Zhang Caiming Xiong Yoshua Bengio 35 52 0 18 Nov 2019
Adversarial Examples in Modern Machine Learning: A Review R. Wiyatno Anqi Xu Ousmane Amadou Dia A. D. Berker AAML 18 104 0 13 Nov 2019
Imperceptible Adversarial Attacks on Tabular Data Vincent Ballet X. Renard Jonathan Aigrain Thibault Laugel P. Frossard Marcin Detyniecki 12 72 0 08 Nov 2019
Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems Guangke Chen Sen Chen Lingling Fan Xiaoning Du Zhe Zhao Fu Song Yang Liu AAML 19 193 0 03 Nov 2019
Enhancing Certifiable Robustness via a Deep Model Ensemble Huan Zhang Minhao Cheng Cho-Jui Hsieh 33 9 0 31 Oct 2019
Adversarial Example in Remote Sensing Image Recognition Li Chen Guowei Zhu Qi Li Haifeng Li AAML 18 26 0 29 Oct 2019
Algorithmic decision-making in AVs: Understanding ethical and technical concerns for smart cities H. S. M. Lim Araz Taeihagh 24 82 0 29 Oct 2019
Detection of Adversarial Attacks and Characterization of Adversarial Subspace Mohammad Esmaeilpour P. Cardinal Alessandro Lameiras Koerich AAML 24 17 0 26 Oct 2019
An Alternative Surrogate Loss for PGD-based Adversarial Testing Sven Gowal J. Uesato Chongli Qin Po-Sen Huang Timothy A. Mann Pushmeet Kohli AAML 50 89 0 21 Oct 2019
A New Defense Against Adversarial Images: Turning a Weakness into a Strength Tao Yu Shengyuan Hu Chuan Guo Wei-Lun Chao Kilian Q. Weinberger AAML 58 101 0 16 Oct 2019
Deep Neural Rejection against Adversarial Examples Angelo Sotgiu Ambra Demontis Marco Melis Battista Biggio Giorgio Fumera Xiaoyi Feng Fabio Roli AAML 19 68 0 01 Oct 2019
Role of Spatial Context in Adversarial Robustness for Object Detection Aniruddha Saha Akshayvarun Subramanya Koninika Patil Hamed Pirsiavash ObjD AAML 32 53 0 30 Sep 2019
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks Rémi Bernhard Pierre-Alain Moëllic J. Dutertre AAML MQ 24 18 0 27 Sep 2019
Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks Tianyu Pang Kun Xu Jun Zhu AAML 28 103 0 25 Sep 2019
Defending Against Physically Realizable Attacks on Image Classification Tong Wu Liang Tong Yevgeniy Vorobeychik AAML 19 125 0 20 Sep 2019
Adversarial Learning with Margin-based Triplet Embedding Regularization Yaoyao Zhong Weihong Deng AAML 28 50 0 20 Sep 2019
Toward Robust Image Classification Basemah Alshemali Alta Graham Jugal Kalita AAML 40 6 0 19 Sep 2019
Sparse and Imperceivable Adversarial Attacks Francesco Croce Matthias Hein AAML 39 199 0 11 Sep 2019
When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures Gil Fidel Ron Bitton A. Shabtai FAtt GAN 21 119 0 08 Sep 2019
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Tianlin Li Siyue Wang Pin-Yu Chen Yanzhi Wang Brian Kulis Xue Lin S. Chin AAML 13 42 0 20 Aug 2019
Implicit Deep Learning L. Ghaoui Fangda Gu Bertrand Travacca Armin Askari Alicia Y. Tsai AI4CE 34 176 0 17 Aug 2019
Defending Against Adversarial Iris Examples Using Wavelet Decomposition Sobhan Soleymani Ali Dabouei J. Dawson Nasser M. Nasrabadi AAML 27 9 0 08 Aug 2019