Adversarial Examples Are Not Bugs, They Are Features

6 May 2019

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

50 / 297 papers shown

Title
Saliency Guided Adversarial Training for Learning Generalizable Features with Applications to Medical Imaging Classification System Xin Li Yao Qiang Chengyin Li Sijia Liu D. Zhu OOD MedIm 29 4 0 09 Sep 2022
Unrestricted Adversarial Samples Based on Non-semantic Feature Clusters Substitution Ming-Kuai Zhou Xiaobing Pei AAML 16 0 0 31 Aug 2022
Shortcut Learning of Large Language Models in Natural Language Understanding Mengnan Du Fengxiang He Na Zou Dacheng Tao Xia Hu KELM OffRL 28 84 0 25 Aug 2022
Improving Adversarial Robustness via Mutual Information Estimation Dawei Zhou Nannan Wang Xinbo Gao Bo Han Xiaoyu Wang Yibing Zhan Tongliang Liu AAML 16 15 0 25 Jul 2022
Can we achieve robustness from data alone? Nikolaos Tsilivis Jingtong Su Julia Kempe OOD DD 36 18 0 24 Jul 2022
Decorrelative Network Architecture for Robust Electrocardiogram Classification Christopher Wiedeman Ge Wang OOD 13 2 0 19 Jul 2022
On the Robustness and Anomaly Detection of Sparse Neural Networks Morgane Ayle Bertrand Charpentier John Rachwan Daniel Zügner Simon Geisler Stephan Günnemann AAML 52 3 0 09 Jul 2022
How many perturbations break this model? Evaluating robustness beyond adversarial accuracy R. Olivier Bhiksha Raj AAML 29 5 0 08 Jul 2022
Removing Batch Normalization Boosts Adversarial Training Haotao Wang Aston Zhang Shuai Zheng Xingjian Shi Mu Li Zhangyang Wang 32 41 0 04 Jul 2022
Auditing Visualizations: Transparency Methods Struggle to Detect Anomalous Behavior Jean-Stanislas Denain Jacob Steinhardt AAML 33 7 0 27 Jun 2022
Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection Systems D'Jeff K. Nkashama Ariana Soltani Jean-Charles Verdier Marc Frappier Pierre-Marting Tardif F. Kabanza OOD AAML 21 5 0 25 Jun 2022
On the Role of Generalization in Transferability of Adversarial Examples Yilin Wang Farzan Farnia AAML 24 10 0 18 Jun 2022
A Meta-Analysis of Distributionally-Robust Models Ben Feuer Ameya Joshi C. Hegde OOD VLM 35 3 0 15 Jun 2022
SeATrans: Learning Segmentation-Assisted diagnosis model via Transformer Junde Wu Huihui Fang Fangxin Shang Dalu Yang Zhao-Yang Wang Jing Gao Yehui Yang Yanwu Xu MedIm ViT 17 19 0 12 Jun 2022
Meet You Halfway: Explaining Deep Learning Mysteries Oriel BenShmuel AAML FedML FAtt OOD 22 0 0 09 Jun 2022
Adversarial Reprogramming Revisited Matthias Englert R. Lazic AAML 21 8 0 07 Jun 2022
Building Robust Ensembles via Margin Boosting Dinghuai Zhang Hongyang R. Zhang Aaron Courville Yoshua Bengio Pradeep Ravikumar A. Suggala AAML UQCV 40 15 0 07 Jun 2022
Vanilla Feature Distillation for Improving the Accuracy-Robustness Trade-Off in Adversarial Training Guodong Cao Zhibo Wang Xiaowei Dong Zhifei Zhang Hengchang Guo Zhan Qin Kui Ren AAML 24 1 0 05 Jun 2022
Optimizing Relevance Maps of Vision Transformers Improves Robustness Hila Chefer Idan Schwartz Lior Wolf ViT 32 37 0 02 Jun 2022
Exact Feature Collisions in Neural Networks Utku Ozbulak Manvel Gasparyan Shodhan Rao W. D. Neve Arnout Van Messem AAML 19 1 0 31 May 2022
One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks Shutong Wu Sizhe Chen Cihang Xie X. Huang AAML 42 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 13 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 22 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 30 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 19 5 0 26 Apr 2022
Synthesizing Adversarial Visual Scenarios for Model-Based Robotic Control Shubhankar Agarwal Sandeep P. Chinchali AAML 27 4 0 13 Apr 2022
Self-Supervised Losses for One-Class Textual Anomaly Detection Kimberly T. Mai Toby O. Davies Lewis D. Griffin 12 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 40 29 0 11 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 31 42 0 30 Mar 2022
Invariance Learning based on Label Hierarchy S. Toyota Kenji Fukumizu OOD 19 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 49 71 0 26 Mar 2022
The Dark Side: Security Concerns in Machine Learning for EDA Zhiyao Xie Jingyu Pan Chen-Chia Chang Yiran Chen 6 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 24 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 24 14 0 13 Mar 2022
Why adversarial training can hurt robust accuracy Jacob Clarysse Julia Hörrmann Fanny Yang AAML 13 18 0 03 Mar 2022
Limitations of Deep Learning for Inverse Problems on Digital Hardware Holger Boche Adalbert Fono Gitta Kutyniok 24 25 0 28 Feb 2022
LPF-Defense: 3D Adversarial Defense based on Frequency Analysis Hanieh Naderi Kimia Noorbakhsh Arian Etemadi S. Kasaei AAML 14 12 0 23 Feb 2022
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition Tianyu Pang Min-Bin Lin Xiao Yang Junyi Zhu Shuicheng Yan 19 119 0 21 Feb 2022
D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles Ashish Hooda Neal Mangaokar Ryan Feng Kassem Fawaz S. Jha Atul Prakash 24 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 24 20 0 02 Feb 2022
Datamodels: Predicting Predictions from Training Data Andrew Ilyas Sung Min Park Logan Engstrom Guillaume Leclerc A. Madry TDI 38 130 0 01 Feb 2022
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 28 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 34 15 0 28 Dec 2021
How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness? Xinhsuai Dong Anh Tuan Luu Min-Bin Lin Shuicheng Yan Hanwang Zhang SILM AAML 20 55 0 22 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 15 13 0 14 Dec 2021
Image classifiers can not be made robust to small perturbations Zheng Dai David K Gifford VLM AAML 16 1 0 07 Dec 2021