Adversarial Examples Are Not Bugs, They Are Features

6 May 2019

Papers citing "Adversarial Examples Are Not Bugs, They Are Features"

50 / 373 papers shown

Title
One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks Shutong Wu Sizhe Chen Cihang Xie Xiaolin Huang AAML 51 27 0 24 May 2022
Sample Complexity Bounds for Robustly Learning Decision Lists against Evasion Attacks Pascale Gourdeau Varun Kanade Marta Z. Kwiatkowska J. Worrell AAML 21 5 0 12 May 2022
How Does Frequency Bias Affect the Robustness of Neural Image Classifiers against Common Corruption and Adversarial Perturbations? Alvin Chan Yew-Soon Ong Clement Tan AAML 24 13 0 09 May 2022
Adversarial Training for High-Stakes Reliability Daniel M. Ziegler Seraphina Nix Lawrence Chan Tim Bauman Peter Schmidt-Nielsen ... Noa Nabeshima Benjamin Weinstein-Raun D. Haas Buck Shlegeris Nate Thomas AAML 38 59 0 03 May 2022
SSR-GNNs: Stroke-based Sketch Representation with Graph Neural Networks Sheng Cheng Yi Ren Yezhou Yang 56 2 0 27 Apr 2022
On Fragile Features and Batch Normalization in Adversarial Training Nils Philipp Walter David Stutz Bernt Schiele AAML 27 5 0 26 Apr 2022
Synthesizing Adversarial Visual Scenarios for Model-Based Robotic Control Shubhankar Agarwal Sandeep Chinchali AAML 40 4 0 13 Apr 2022
Self-Supervised Losses for One-Class Textual Anomaly Detection Kimberly T. Mai Toby O. Davies Lewis D. Griffin 20 7 0 12 Apr 2022
Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization Jungbeom Lee Eunji Kim J. Mok Sung-Hoon Yoon WSOL 42 29 0 11 Apr 2022
SkeleVision: Towards Adversarial Resiliency of Person Tracking with Multi-Task Learning Nilaksh Das ShengYun Peng Duen Horng Chau AAML 30 2 0 02 Apr 2022
Investigating Top- $k$ White-Box and Transferable Black-box Attack Chaoning Zhang Philipp Benz Adil Karjauv Jae-Won Cho Kang Zhang In So Kweon 33 43 0 30 Mar 2022
Invariance Learning based on Label Hierarchy S. Toyota Kenji Fukumizu OOD 23 1 0 29 Mar 2022
A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies Zhuang Qian Kaizhu Huang Qiufeng Wang Xu-Yao Zhang OOD AAML ObjD 54 72 0 26 Mar 2022
The Dark Side: Security Concerns in Machine Learning for EDA Zhiyao Xie Jingyu Pan Chen-Chia Chang Yiran Chen 16 4 0 20 Mar 2022
Attacking deep networks with surrogate-based adversarial black-box methods is easy Nicholas A. Lord Romain Mueller Luca Bertinetto AAML MLAU 19 25 0 16 Mar 2022
LAS-AT: Adversarial Training with Learnable Attack Strategy Xiaojun Jia Yong Zhang Baoyuan Wu Ke Ma Jue Wang Xiaochun Cao AAML 47 131 0 13 Mar 2022
AutoGPart: Intermediate Supervision Search for Generalizable 3D Part Segmentation Xueyi Liu Xiaomeng Xu Anyi Rao Chuang Gan L. Yi 3DPC 26 14 0 13 Mar 2022
Why adversarial training can hurt robust accuracy Jacob Clarysse Julia Hörrmann Fanny Yang AAML 15 18 0 03 Mar 2022
Limitations of Deep Learning for Inverse Problems on Digital Hardware Holger Boche Adalbert Fono Gitta Kutyniok 32 25 0 28 Feb 2022
LPF-Defense: 3D Adversarial Defense based on Frequency Analysis Hanieh Naderi Kimia Noorbakhsh Arian Etemadi S. Kasaei AAML 16 12 0 23 Feb 2022
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition Tianyu Pang Min Lin Xiao Yang Junyi Zhu Shuicheng Yan 32 120 0 21 Feb 2022
Critical Checkpoints for Evaluating Defence Models Against Adversarial Attack and Robustness Kanak Tekwani Manojkumar Somabhai Parmar AAML ELM 13 0 0 18 Feb 2022
D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles Ashish Hooda Neal Mangaokar Ryan Feng Kassem Fawaz S. Jha Atul Prakash 29 11 0 11 Feb 2022
Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization Xiaojun Xu Jacky Y. Zhang Evelyn Ma Danny Son Oluwasanmi Koyejo Bo-wen Li 20 10 0 03 Feb 2022
Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks A. Harrington Arturo Deza OOD AAML 29 20 0 02 Feb 2022
Datamodels: Predicting Predictions from Training Data Andrew Ilyas Sung Min Park Logan Engstrom Guillaume Leclerc A. Madry TDI 52 131 0 01 Feb 2022
Can Adversarial Training Be Manipulated By Non-Robust Features? Lue Tao Lei Feng Hongxin Wei Jinfeng Yi Sheng-Jun Huang Songcan Chen AAML 136 16 0 31 Jan 2022
On the Robustness of Quality Measures for GANs Motasem Alfarra Juan C. Pérez Anna Frühstück Philip Torr Peter Wonka Guohao Li AAML EGVM 102 10 0 31 Jan 2022
Boosting 3D Adversarial Attacks with Attacking On Frequency Binbin Liu Jinlai Zhang Lyujie Chen Jihong Zhu 3DPC 19 36 0 26 Jan 2022
Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions Marwan Omar Soohyeon Choi Daehun Nyang David A. Mohaisen 32 57 0 03 Jan 2022
On Distinctive Properties of Universal Perturbations Sung Min Park K. Wei Kai Y. Xiao Jungshian Li A. Madry AAML 22 2 0 31 Dec 2021
Closer Look at the Transferability of Adversarial Examples: How They Fool Different Models Differently Futa Waseda Sosuke Nishikawa Trung-Nghia Le H. Nguyen Isao Echizen SILM 36 35 0 29 Dec 2021
DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification A. Ćiprijanović Diana Kafkes Gregory F. Snyder F. Sánchez G. Perdue K. Pedro Brian D. Nord Sandeep Madireddy Stefan M. Wild AAML 42 15 0 28 Dec 2021
How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness? Xinhsuai Dong Anh Tuan Luu Min Lin Shuicheng Yan Hanwang Zhang SILM AAML 20 55 0 22 Dec 2021
Improving Robustness with Image Filtering M. Terzi Mattia Carletti Gian Antonio Susto AAML 29 0 0 21 Dec 2021
Measure and Improve Robustness in NLP Models: A Survey Xuezhi Wang Haohan Wang Diyi Yang 139 130 0 15 Dec 2021
On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training Chen Liu Zhichao Huang Mathieu Salzmann Tong Zhang Sabine Süsstrunk AAML 23 13 0 14 Dec 2021
Image classifiers can not be made robust to small perturbations Zheng Dai David K Gifford VLM AAML 36 1 0 07 Dec 2021
Fighting Fire with Fire: Contrastive Debiasing without Bias-free Data via Generative Bias-transformation Yeonsung Jung Hajin Shim J. Yang Eunho Yang 29 8 0 02 Dec 2021
Adv-4-Adv: Thwarting Changing Adversarial Perturbations via Adversarial Domain Adaptation Tianyue Zheng Zhe Chen Shuya Ding Chao Cai Jun Luo AAML 35 5 0 01 Dec 2021
Adversarial Attacks in Cooperative AI Ted Fujimoto Arthur Paul Pedersen AAML 27 2 0 29 Nov 2021
Towards Understanding the Impact of Model Size on Differential Private Classification Yinchen Shen Zhiguo Wang Ruoyu Sun Xiaojing Shen 27 11 0 27 Nov 2021
Medical Aegis: Robust adversarial protectors for medical images Qingsong Yao Zecheng He S. Kevin Zhou AAML MedIm 30 2 0 22 Nov 2021
Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectors Ruoxi Sun Minhui Xue Gareth Tyson Tian Dong Shaofeng Li Shuo Wang Haojin Zhu S. Çamtepe Surya Nepal AAML 49 15 0 19 Nov 2021
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps Muhammad Awais Fengwei Zhou Chuanlong Xie Jiawei Li Sung-Ho Bae Zhenguo Li AAML 43 17 0 09 Nov 2021
Natural Adversarial Objects Felix Lau Nishant Subramani Sasha Harrison Aerin Kim E. Branson Rosanne Liu 24 7 0 07 Nov 2021
Toward Learning Human-aligned Cross-domain Robust Models by Countering Misaligned Features Haohan Wang Zeyi Huang Hanlin Zhang Yong Jae Lee Eric P. Xing OOD 138 16 0 05 Nov 2021
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks Maksym Yatsura J. H. Metzen Matthias Hein OOD 26 14 0 02 Nov 2021
Self-Supervised Learning Disentangled Group Representation as Feature Tan Wang Zhongqi Yue Jianqiang Huang Qianru Sun Hanwang Zhang OOD 36 67 0 28 Oct 2021
Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks Hassan Dbouk Naresh R Shanbhag AAML 21 7 0 28 Oct 2021