Detecting Adversarial Samples from Artifacts

1 March 2017

Papers citing "Detecting Adversarial Samples from Artifacts"

50 / 166 papers shown

Title
Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks Yonggan Fu Qixuan Yu Yang Zhang Shan-Hung Wu Ouyang Xu David D. Cox Yingyan Lin AAML OOD 33 29 0 26 Oct 2021
MPSN: Motion-aware Pseudo Siamese Network for Indoor Video Head Detection in Buildings Kailai Sun Xiaoteng Ma Peng Liu Qianchuan Zhao 3DPC AAML 25 11 0 07 Oct 2021
A Uniform Framework for Anomaly Detection in Deep Neural Networks Fangzhen Zhao Chenyi Zhang Naipeng Dong Zefeng You Zhenxin Wu AAML OOD OODD 30 9 0 06 Oct 2021
TREATED:Towards Universal Defense against Textual Adversarial Attacks Bin Zhu Zhaoquan Gu Le Wang Zhihong Tian AAML 36 8 0 13 Sep 2021
2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency Yonggan Fu Yang Katie Zhao Qixuan Yu Chaojian Li Yingyan Lin AAML 52 12 0 11 Sep 2021
Exploiting Multi-Object Relationships for Detecting Adversarial Attacks in Complex Scenes Mingjun Yin Shasha Li Zikui Cai Chengyu Song Ulugbek S. Kamilov Amit K. Roy-Chowdhury S. Krishnamurthy AAML 19 18 0 19 Aug 2021
A Survey on Open Set Recognition Atefeh Mahdavi Marco M. Carvalho BDL 29 35 0 18 Aug 2021
Optical Adversarial Attack Abhiram Gnanasambandam A. Sherman Stanley H. Chan AAML 35 65 0 13 Aug 2021
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them Florian Tramèr AAML 30 65 0 24 Jul 2021
Unsupervised Detection of Adversarial Examples with Model Explanations Gihyuk Ko Gyumin Lim AAML GAN 39 5 0 22 Jul 2021
Improving black-box optimization in VAE latent space using decoder uncertainty Pascal Notin José Miguel Hernández-Lobato Y. Gal 32 61 0 30 Jun 2021
Adversarial Examples in Multi-Layer Random ReLU Networks Peter L. Bartlett Sébastien Bubeck Yeshwanth Cherapanamjeri AAML GAN 32 28 0 23 Jun 2021
Adversarial Robustness via Fisher-Rao Regularization Marine Picot Francisco Messina Malik Boudiaf Fabrice Labeau Ismail Ben Ayed Pablo Piantanida AAML 31 23 0 12 Jun 2021
Taxonomy of Machine Learning Safety: A Survey and Primer Sina Mohseni Haotao Wang Zhiding Yu Chaowei Xiao Zhangyang Wang J. Yadawa 26 31 0 09 Jun 2021
A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks Jacob Mitchell Springer Melanie Mitchell Garrett Kenyon AAML 31 43 0 03 Jun 2021
Stochastic-Shield: A Probabilistic Approach Towards Training-Free Adversarial Defense in Quantized CNNs Lorena Qendro Sangwon Ha R. D. Jong Partha P. Maji AAML FedML MQ 21 7 0 13 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
Relating Adversarially Robust Generalization to Flat Minima David Stutz Matthias Hein Bernt Schiele OOD 38 65 0 09 Apr 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
Ensemble-in-One: Learning Ensemble within Random Gated Networks for Enhanced Adversarial Robustness Yi Cai Xuefei Ning Huazhong Yang Yu Wang AAML 27 4 0 27 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
Low Curvature Activations Reduce Overfitting in Adversarial Training Vasu Singla Sahil Singla David Jacobs S. Feizi AAML 37 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 38 133 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
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples Sven Gowal Chongli Qin J. Uesato Timothy A. Mann Pushmeet Kohli AAML 17 324 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
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
Adversarially Robust Neural Architectures Minjing Dong Yanxi Li Yunhe Wang Chang Xu AAML OOD 42 48 0 02 Sep 2020
Adversarial Examples on Object Recognition: A Comprehensive Survey A. Serban E. Poll Joost Visser AAML 29 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
Backdoor Learning: A Survey Yiming Li Yong Jiang Zhifeng Li Shutao Xia AAML 45 590 0 17 Jul 2020
SINVAD: Search-based Image Space Navigation for DNN Image Classifier Test Input Generation Sungmin Kang R. Feldt S. Yoo AAML 26 32 0 19 May 2020
Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty Xiyue Zhang Xiaofei Xie Lei Ma Xiaoning Du Q. Hu Yang Liu Jianjun Zhao Meng Sun AAML 16 76 0 24 Apr 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
Probabilistic Safety for Bayesian Neural Networks Matthew Wicker Luca Laurenti A. Patané Marta Z. Kwiatkowska AAML 14 52 0 21 Apr 2020
Certifying Joint Adversarial Robustness for Model Ensembles M. Jonas David Evans AAML 21 2 0 21 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
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
Overfitting in adversarially robust deep learning Leslie Rice Eric Wong Zico Kolter 47 787 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 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
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
Attacking Optical Character Recognition (OCR) Systems with Adversarial Watermarks Lu Chen Wenyuan Xu AAML 24 21 0 08 Feb 2020
Analysis of Random Perturbations for Robust Convolutional Neural Networks Adam Dziedzic S. Krishnan OOD AAML 24 1 0 08 Feb 2020
Fast is better than free: Revisiting adversarial training Eric Wong Leslie Rice J. Zico Kolter AAML OOD 99 1,159 0 12 Jan 2020
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
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
Revealing Perceptible Backdoors, without the Training Set, via the Maximum Achievable Misclassification Fraction Statistic Zhen Xiang David J. Miller Hang Wang G. Kesidis AAML 34 9 0 18 Nov 2019