On Detecting Adversarial Perturbations

14 February 2017

Papers citing "On Detecting Adversarial Perturbations"

50 / 187 papers shown

Title
Inconspicuous Adversarial Patches for Fooling Image Recognition Systems on Mobile Devices Tao Bai Jinqi Luo Jun Zhao AAML 31 30 0 29 Jun 2021
Evaluating the Robustness of Trigger Set-Based Watermarks Embedded in Deep Neural Networks Suyoung Lee Wonho Song Suman Jana M. Cha Sooel Son AAML 27 13 0 18 Jun 2021
A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks Jacob Mitchell Springer Melanie Mitchell Garrett Kenyon AAML 33 43 0 03 Jun 2021
NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels Jingfeng Zhang Xilie Xu Bo Han Tongliang Liu Gang Niu Li-zhen Cui Masashi Sugiyama NoLa AAML 23 9 0 31 May 2021
Salient Feature Extractor for Adversarial Defense on Deep Neural Networks Jinyin Chen Ruoxi Chen Haibin Zheng Zhaoyan Ming Wenrong Jiang Chen Cui AAML 25 10 0 14 May 2021
Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning Matthew Watson Noura Al Moubayed AAML MedIm 12 20 0 05 May 2021
Metamorphic Detection of Repackaged Malware S. Singh Gail E. Kaiser 24 8 0 27 Apr 2021
Relating Adversarially Robust Generalization to Flat Minima David Stutz Matthias Hein Bernt Schiele OOD 41 65 0 09 Apr 2021
Performance Analysis of Out-of-Distribution Detection on Various Trained Neural Networks Jens Henriksson C. Berger Markus Borg Lars Tornberg S. Sathyamoorthy Cristofer Englund OODD 22 17 0 29 Mar 2021
LiBRe: A Practical Bayesian Approach to Adversarial Detection Zhijie Deng Xiao Yang Shizhen Xu Hang Su Jun Zhu BDL AAML 25 61 0 27 Mar 2021
MagDR: Mask-guided Detection and Reconstruction for Defending Deepfakes Zhikai Chen Lingxi Xie Shanmin Pang Yong He Bo Zhang AAML 36 32 0 26 Mar 2021
Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack Mengting Xu Tao Zhang Zhongnian Li Mingxia Liu Daoqiang Zhang AAML OOD MedIm 33 41 0 05 Mar 2021
SpectralDefense: Detecting Adversarial Attacks on CNNs in the Fourier Domain P. Harder Franz-Josef Pfreundt M. Keuper J. Keuper AAML 27 48 0 04 Mar 2021
Automated Discovery of Adaptive Attacks on Adversarial Defenses Chengyuan Yao Pavol Bielik Petar Tsankov Martin Vechev AAML 19 24 0 23 Feb 2021
Towards Adversarial-Resilient Deep Neural Networks for False Data Injection Attack Detection in Power Grids Jiangnan Li Yingyuan Yang Jinyuan Stella Sun K. Tomsovic Hairong Qi AAML 41 14 0 17 Feb 2021
Low Curvature Activations Reduce Overfitting in Adversarial Training Vasu Singla Sahil Singla David Jacobs S. Feizi AAML 43 45 0 15 Feb 2021
Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS Felix O. Olowononi D. Rawat Chunmei Liu 38 134 0 14 Feb 2021
Dompteur: Taming Audio Adversarial Examples Thorsten Eisenhofer Lea Schonherr Joel Frank Lars Speckemeier D. Kolossa Thorsten Holz AAML 39 24 0 10 Feb 2021
Benford's law: what does it say on adversarial images? João G. Zago Fabio L. Baldissera Eric A. Antonelo Rodrigo T. Saad AAML 19 2 0 09 Feb 2021
Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression Masanori Yamada Sekitoshi Kanai Tomoharu Iwata Tomokatsu Takahashi Yuki Yamanaka Hiroshi Takahashi Atsutoshi Kumagai AAML 16 9 0 05 Feb 2021
Locally optimal detection of stochastic targeted universal adversarial perturbations Amish Goel P. Moulin AAML 19 2 0 08 Dec 2020
Adversarial Classification: Necessary conditions and geometric flows Nicolas García Trillos Ryan W. Murray AAML 37 19 0 21 Nov 2020
Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations Jinyuan Jia Binghui Wang Xiaoyu Cao Hongbin Liu Neil Zhenqiang Gong 21 24 0 15 Nov 2020
GreedyFool: Distortion-Aware Sparse Adversarial Attack Xiaoyi Dong Dongdong Chen Jianmin Bao Chuan Qin Lu Yuan Weiming Zhang Nenghai Yu Dong Chen AAML 18 63 0 26 Oct 2020
A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning Hongjun Wang Guanbin Li Xiaobai Liu Liang Lin GAN AAML 21 22 0 15 Oct 2020
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples Sven Gowal Chongli Qin J. Uesato Timothy A. Mann Pushmeet Kohli AAML 22 325 0 07 Oct 2020
Block-wise Image Transformation with Secret Key for Adversarially Robust Defense Maungmaung Aprilpyone Hitoshi Kiya 29 57 0 02 Oct 2020
Generating Adversarial yet Inconspicuous Patches with a Single Image Jinqi Luo Tao Bai Jun Zhao AAML 27 6 0 21 Sep 2020
Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective G. R. Machado Eugênio Silva R. Goldschmidt AAML 33 157 0 08 Sep 2020
Adversarial Examples on Object Recognition: A Comprehensive Survey A. Serban E. Poll Joost Visser AAML 32 73 0 07 Aug 2020
Cassandra: Detecting Trojaned Networks from Adversarial Perturbations Xiaoyu Zhang Ajmal Mian Rohit Gupta Nazanin Rahnavard M. Shah AAML 34 26 0 28 Jul 2020
RANDOM MASK: Towards Robust Convolutional Neural Networks Tiange Luo Tianle Cai Mengxiao Zhang Siyu Chen Liwei Wang AAML OOD 24 17 0 27 Jul 2020
Adversarial Example Games A. Bose Gauthier Gidel Hugo Berrard Andre Cianflone Pascal Vincent Simon Lacoste-Julien William L. Hamilton AAML GAN 38 51 0 01 Jul 2020
Towards Robust Pattern Recognition: A Review Xu-Yao Zhang Cheng-Lin Liu C. Suen OOD HAI 26 103 0 12 Jun 2020
Exploring the role of Input and Output Layers of a Deep Neural Network in Adversarial Defense Jay N. Paranjape R. Dubey Vijendran V. Gopalan AAML 25 2 0 02 Jun 2020
PatchGuard: A Provably Robust Defense against Adversarial Patches via Small Receptive Fields and Masking Chong Xiang A. Bhagoji Vikash Sehwag Prateek Mittal AAML 30 29 0 17 May 2020
Adversarial Attacks and Defenses: An Interpretation Perspective Ninghao Liu Mengnan Du Ruocheng Guo Huan Liu Xia Hu AAML 31 8 0 23 Apr 2020
Single-step Adversarial training with Dropout Scheduling S. VivekB. R. Venkatesh Babu OOD AAML 18 71 0 18 Apr 2020
Towards Robust Classification with Image Quality Assessment Yeli Feng Yiyu Cai 19 0 0 14 Apr 2020
DaST: Data-free Substitute Training for Adversarial Attacks Mingyi Zhou Jing Wu Yipeng Liu Shuaicheng Liu Ce Zhu 25 142 0 28 Mar 2020
What Deep CNNs Benefit from Global Covariance Pooling: An Optimization Perspective Qilong Wang Li Zhang Banggu Wu Dongwei Ren P. Li W. Zuo Q. Hu 22 21 0 25 Mar 2020
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
Toward Adversarial Robustness via Semi-supervised Robust Training Yiming Li Baoyuan Wu Yan Feng Yanbo Fan Yong Jiang Zhifeng Li Shutao Xia AAML 87 13 0 16 Mar 2020
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization Saehyung Lee Hyungyu Lee Sungroh Yoon AAML 163 113 0 05 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 72 63 0 02 Mar 2020
Utilizing Network Properties to Detect Erroneous Inputs Matt Gorbett Nathaniel Blanchard AAML 23 6 0 28 Feb 2020
Overfitting in adversarially robust deep learning Leslie Rice Eric Wong Zico Kolter 47 788 0 26 Feb 2020
Adversarial Ranking Attack and Defense Mo Zhou Zhenxing Niu Le Wang Qilin Zhang G. Hua 36 38 0 26 Feb 2020
Non-Intrusive Detection of Adversarial Deep Learning Attacks via Observer Networks K. Sivamani R. Sahay Aly El Gamal AAML 16 3 0 22 Feb 2020
Deflecting Adversarial Attacks Yao Qin Nicholas Frosst Colin Raffel G. Cottrell Geoffrey E. Hinton AAML 30 15 0 18 Feb 2020