Adversarial Examples Are Not Bugs, They Are Features

6 May 2019

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

50 / 372 papers shown

Title
Practical Galaxy Morphology Tools from Deep Supervised Representation Learning Mike Walmsley Anna M. M. Scaife Chris J. Lintott Michelle Lochner Yu Zhu ... Xibo Ma Sandor Kruk Zhen Lei G. Guo B. Simmons 24 29 0 25 Oct 2021
Generalized Out-of-Distribution Detection: A Survey Jingkang Yang Kaiyang Zhou Yixuan Li Ziwei Liu 193 879 0 21 Oct 2021
RoMA: a Method for Neural Network Robustness Measurement and Assessment Natan Levy Guy Katz OOD AAML 12 13 0 21 Oct 2021
No One Representation to Rule Them All: Overlapping Features of Training Methods Raphael Gontijo-Lopes Yann N. Dauphin E. D. Cubuk 32 60 0 20 Oct 2021
Combining Diverse Feature Priors Saachi Jain Dimitris Tsipras A. Madry 69 14 0 15 Oct 2021
Amicable examples for informed source separation Naoya Takahashi Yuki Mitsufuji AAML 33 1 0 11 Oct 2021
Source Mixing and Separation Robust Audio Steganography Naoya Takahashi M. Singh Yuki Mitsufuji 34 6 0 11 Oct 2021
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks Hanxun Huang Yisen Wang S. Erfani Quanquan Gu James Bailey Xingjun Ma AAML TPM 46 100 0 07 Oct 2021
Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting Chengyu Dong Liyuan Liu Jingbo Shang NoLa AAML 66 18 0 07 Oct 2021
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs Philipp Benz Soomin Ham Chaoning Zhang Adil Karjauv In So Kweon AAML ViT 47 79 0 06 Oct 2021
Adversarial defenses via a mixture of generators Maciej Żelaszczyk Jacek Mańdziuk AAML 13 0 0 05 Oct 2021
Classification and Adversarial examples in an Overparameterized Linear Model: A Signal Processing Perspective Adhyyan Narang Vidya Muthukumar A. Sahai SILM AAML 36 1 0 27 Sep 2021
Modeling Adversarial Noise for Adversarial Training Dawei Zhou Nannan Wang Bo Han Tongliang Liu AAML 38 15 0 21 Sep 2021
The mathematics of adversarial attacks in AI -- Why deep learning is unstable despite the existence of stable neural networks Alexander Bastounis A. Hansen Verner Vlacic AAML OOD 32 28 0 13 Sep 2021
Training Meta-Surrogate Model for Transferable Adversarial Attack Yunxiao Qin Yuanhao Xiong Jinfeng Yi Cho-Jui Hsieh AAML 20 18 0 05 Sep 2021
Adversarial Robustness for Unsupervised Domain Adaptation Muhammad Awais Fengwei Zhou Hang Xu Lanqing Hong Ping Luo Sung-Ho Bae Zhenguo Li 20 39 0 02 Sep 2021
Impact of Attention on Adversarial Robustness of Image Classification Models Prachi Agrawal Narinder Singh Punn S. K. Sonbhadra Sonali Agarwal AAML 24 6 0 02 Sep 2021
How Does Adversarial Fine-Tuning Benefit BERT? J. Ebrahimi Hao Yang Wei Zhang AAML 26 4 0 31 Aug 2021
Disrupting Adversarial Transferability in Deep Neural Networks Christopher Wiedeman Ge Wang AAML 36 8 0 27 Aug 2021
AdvDrop: Adversarial Attack to DNNs by Dropping Information Ranjie Duan YueFeng Chen Dantong Niu Yun Yang •. A. K. Qin Yuan He AAML 24 89 0 20 Aug 2021
Causal Attention for Unbiased Visual Recognition Tan Wang Chan Zhou Qianru Sun Hanwang Zhang OOD CML 34 109 0 19 Aug 2021
Exploiting Features with Split-and-Share Module Jaemin Lee Min-seok Seo Jongchan Park Dong-Geol Choi 24 0 0 10 Aug 2021
Advances in adversarial attacks and defenses in computer vision: A survey Naveed Akhtar Ajmal Mian Navid Kardan M. Shah AAML 36 236 0 01 Aug 2021
Who's Afraid of Thomas Bayes? Erick Galinkin AAML 28 0 0 30 Jul 2021
Dataset Distillation with Infinitely Wide Convolutional Networks Timothy Nguyen Roman Novak Lechao Xiao Jaehoon Lee DD 51 231 0 27 Jul 2021
The Values Encoded in Machine Learning Research Abeba Birhane Pratyusha Kalluri Dallas Card William Agnew Ravit Dotan Michelle Bao 41 274 0 29 Jun 2021
Can contrastive learning avoid shortcut solutions? Joshua Robinson Li Sun Ke Yu Kayhan Batmanghelich Stefanie Jegelka S. Sra SSL 19 142 0 21 Jun 2021
Adversarial Examples Make Strong Poisons Liam H. Fowl Micah Goldblum Ping Yeh-Chiang Jonas Geiping Wojtek Czaja Tom Goldstein SILM 32 132 0 21 Jun 2021
Attack to Fool and Explain Deep Networks Naveed Akhtar M. Jalwana Bennamoun Ajmal Mian AAML 27 33 0 20 Jun 2021
Adversarial Visual Robustness by Causal Intervention Kaihua Tang Ming Tao Hanwang Zhang CML AAML 27 21 0 17 Jun 2021
Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems Giovanni Apruzzese M. Andreolini Luca Ferretti Mirco Marchetti M. Colajanni AAML 34 105 0 17 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
Disrupting Model Training with Adversarial Shortcuts Ivan Evtimov Ian Covert Aditya Kusupati Tadayoshi Kohno AAML 28 10 0 12 Jun 2021
Reveal of Vision Transformers Robustness against Adversarial Attacks Ahmed Aldahdooh W. Hamidouche Olivier Déforges ViT 15 57 0 07 Jun 2021
Understanding Neural Code Intelligence Through Program Simplification Md Rafiqul Islam Rabin Vincent J. Hellendoorn Mohammad Amin Alipour AAML 49 58 0 07 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
Exposing Previously Undetectable Faults in Deep Neural Networks Isaac Dunn Hadrien Pouget Daniel Kroening T. Melham AAML 34 28 0 01 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
Goal Misgeneralization in Deep Reinforcement Learning L. Langosco Jack Koch Lee D. Sharkey J. Pfau Laurent Orseau David M. Krueger 30 78 0 28 May 2021
Exploring Misclassifications of Robust Neural Networks to Enhance Adversarial Attacks Leo Schwinn René Raab A. Nguyen Dario Zanca Bjoern M. Eskofier AAML 14 60 0 21 May 2021
Real-time Detection of Practical Universal Adversarial Perturbations Kenneth T. Co Luis Muñoz-González Leslie Kanthan Emil C. Lupu AAML 33 6 0 16 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
Leveraging Sparse Linear Layers for Debuggable Deep Networks Eric Wong Shibani Santurkar A. Madry FAtt 22 88 0 11 May 2021
Poisoning the Unlabeled Dataset of Semi-Supervised Learning Nicholas Carlini AAML 164 68 0 04 May 2021
An Overview of Laser Injection against Embedded Neural Network Models Mathieu Dumont Pierre-Alain Moëllic R. Viera J. Dutertre Rémi Bernhard AAML 30 9 0 04 May 2021
Impact of Spatial Frequency Based Constraints on Adversarial Robustness Rémi Bernhard Pierre-Alain Moëllic Martial Mermillod Yannick Bourrier Romain Cohendet M. Solinas M. Reyboz AAML 30 17 0 26 Apr 2021
SPECTRE: Defending Against Backdoor Attacks Using Robust Statistics J. Hayase Weihao Kong Raghav Somani Sewoong Oh AAML 24 150 0 22 Apr 2021
Understanding and Avoiding AI Failures: A Practical Guide R. M. Williams Roman V. Yampolskiy 30 24 0 22 Apr 2021
Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answering Corentin Dancette Rémi Cadène Damien Teney Matthieu Cord CML 28 76 0 07 Apr 2021
Universal Adversarial Training with Class-Wise Perturbations Philipp Benz Chaoning Zhang Adil Karjauv In So Kweon AAML 25 26 0 07 Apr 2021