Most cited papers in Adversarial Robustness in Machine Learning
What are the most cited papers in Adversarial Robustness in Machine Learning?
The most cited paper in Adversarial Robustness in Machine Learning is "Rethinking the Inception Architecture for Computer Vision" (Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe et al., 2016), with 31,624 citations. Next come "MobileNetV2: Inverted Residuals and Linear Bottlenecks" (26,808) and "CBAM: Convolutional Block Attention Module" (23,707).
Showing 50 of 98 papers. Every paper with more than 1,340 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Rethinking the Inception Architecture for Computer Vision Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe et al. | Jun 2016 | 31,624 |
| 2 | MobileNetV2: Inverted Residuals and Linear Bottlenecks Mark Sandler, Andrew Howard, Menglong Zhu et al. | Jun 2018 | 26,808 |
| 3 | CBAM: Convolutional Block Attention Module Sanghyun Woo, Jongchan Park, Joon‐Young Lee et al. · Lecture notes in computer science | 2018 | 23,707 |
| 4 | An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov et al. · arXiv (Cornell University) | 22 Oct 2020 | 22,044 |
| 5 | Xception: Deep Learning with Depthwise Separable Convolutions François Chollet | Jul 2017 | 19,596 |
| 6 | Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification Kaiming He, Xiangyu Zhang, Shaoqing Ren et al. | Dec 2015 | 19,129 |
| 7 | "Why Should I Trust You?" Marco Túlio Ribeiro, Sameer Kumar Singh, Carlos Guestrin | 8 Aug 2016 | 16,496 |
| 8 | Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke et al. · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) | 12 Feb 2017 | 12,801 |
| 9 | Aggregated Residual Transformations for Deep Neural Networks Saining Xie, Ross Girshick, Piotr Dollár et al. | Jul 2017 | 12,067 |
| 10 | Identity Mappings in Deep Residual Networks Kaiming He, Xiangyu Zhang, Shaoqing Ren et al. · Lecture notes in computer science | 2016 | 10,296 |
| 11 | Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser et al. · Information Fusion | 26 Dec 2019 | 10,008 |
| 12 | ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks Qilong Wang, Banggu Wu, Pengfei Zhu et al. | Jun 2020 | 8,753 |
| 13 | Explaining and Harnessing Adversarial Examples Ian Goodfellow, Jonathon Shlens, Christian Szegedy · arXiv (Cornell University) | 20 Dec 2014 | 8,140 |
| 14 | Cascade R-CNN: Delving Into High Quality Object Detection Zhaowei Cai, Nuno M. Vasconcelos | Jun 2018 | 7,067 |
| 15 | ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng et al. · Lecture notes in computer science | 2018 | 6,903 |
| 16 | Continuous control with deep reinforcement learning Timothy Lillicrap, Jonathan J. Hunt, Alexander Pritzel et al. · arXiv (Cornell University) | 22 Jul 2016 | 6,781 |
| 17 | TensorFlow: a system for large-scale machine learning Martı́n Abadi et al. · Operating Systems Design and Implementation | 2 Nov 2016 | 6,365 |
| 18 | Deep Learning with Differential Privacy Martı́n Abadi, Andy Chu, Ian Goodfellow et al. | 24 Oct 2016 | 6,312 |
| 19 | Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) Amina Adadi, Mohammed Berrada · IEEE Access | 2018 | 6,253 |
| 20 | Intriguing properties of neural networks Christian Szegedy, Wojciech Zaremba, Ilya Sutskever et al. · arXiv (Cornell University) | 21 Dec 2013 | 5,734 |
| 21 | Learning to Forget: Continual Prediction with LSTM Felix A. Gers, Jürgen Schmidhuber, Fred A. Cummins · Neural Computation | Oct 2000 | 5,583 |
| 22 | “Why Should I Trust You?”: Explaining the Predictions of Any Classifier Marco Ribeiro, Sameer Kumar Singh, Carlos Guestrin | 2016 | 5,530 |
| 23 | DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Pascal Frossard | Jun 2016 | 5,406 |
| 24 | Adversarial Discriminative Domain Adaptation Eric Tzeng, Judy Hoffman, Kate Saenko et al. | Jul 2017 | 5,102 |
| 25 | A survey of methods for explaining black box models Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri et al. · ISTI Open Portal | 2019 | 5,066 |
| 26 | Wasserstein Generative Adversarial Networks Martín Arjovsky, Soumith Chintala, Léon Bottou · International Conference on Machine Learning | 17 Jul 2017 | 5,047 |
| 27 | CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features Sangdoo Yun, Dongyoon Han, Sanghyuk Chun et al. | Oct 2019 | 4,925 |
| 28 | On Assessing ML Model Robustness: A Methodological Framework (Academic Track) Awadid, Afef, Robert, Boris · OASIcs : OpenAccess Series in Informatics | 2025 | 4,781 |
| 29 | Greedy Layer-Wise Training of Deep Networks Yoshua Bengio, Pascal Lamblin, Dan Popovici et al. · The MIT Press eBooks | 7 Sep 2007 | 4,753 |
| 30 | Membership Inference Attacks Against Machine Learning Models Reza Shokri, Marco Stronati, Congzheng Song et al. | May 2017 | 4,547 |
| 31 | AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations Luciano Floridi, Josh Cowls, Monica Beltrametti et al. · Minds and Machines | 25 Nov 2018 | 4,502 |
| 32 | The Limitations of Deep Learning in Adversarial Settings Nicolas Papernot, Patrick Drew McDaniel, Somesh Jha et al. | Mar 2016 | 3,986 |
| 33 | Knowledge Distillation: A Survey Jianping Gou, Baosheng Yu, Stephen J. Maybank et al. · International Journal of Computer Vision | 22 Mar 2021 | 3,806 |
| 34 | Practical Black-Box Attacks against Machine Learning Nicolas Papernot, Patrick Drew McDaniel, Ian Goodfellow et al. | 31 Mar 2017 | 3,549 |
| 35 | Deep Reinforcement Learning with Double Q-Learning Hado van Hasselt, Arthur Guez, David Lawrence Silver · Proceedings of the AAAI Conference on Artificial Intelligence | 2 Mar 2016 | 3,510 |
| 36 | How transferable are features in deep neural networks? Jason Yosinski, Jeff Clune, Yoshua Bengio et al. · arXiv (Cornell University) | 6 Nov 2014 | 3,452 |
| 37 | Trust Region Policy Optimization John Schulman, Sergey Levine, Philipp Moritz et al. · arXiv (Cornell University) | 19 Feb 2015 | 3,119 |
| 38 | Boosting Adversarial Attacks with Momentum Yinpeng Dong, Fangzhou Liao, Tianyu Pang et al. | Jun 2018 | 3,055 |
| 39 | Explainable AI: A Review of Machine Learning Interpretability Methods Pantelis Linardatos, Vasilis Papastefanopoulos, Sotiris B. Kotsiantis · Entropy | 25 Dec 2020 | 2,932 |
| 40 | Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures Matt Fredrikson, Somesh Jha, Thomas Ristenpart | 6 Oct 2015 | 2,905 |
| 41 | Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning Takeru Miyato, Shin‐ichi Maeda, Masanori Koyama et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 23 Jul 2018 | 2,897 |
| 42 | Methods for interpreting and understanding deep neural networks Grégoire Montavon, Wojciech Samek, Klaus‐Robert Müller · Digital Signal Processing | 24 Oct 2017 | 2,780 |
| 43 | Universal Adversarial Perturbations Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi et al. | Jul 2017 | 2,752 |
| 44 | A review of uncertainty quantification in deep learning: Techniques, applications and challenges Moloud Abdar, Farhad Pourpanah, Sadiq Hussain et al. · Information Fusion | 23 May 2021 | 2,702 |
| 45 | Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Yarin Gal, Zoubin Ghahramani · arXiv (Cornell University) | 6 Jun 2015 | 2,679 |
| 46 | Learning Efficient Convolutional Networks through Network Slimming Zhuang Liu, Jianguo Li, Zhiqiang Shen et al. | Oct 2017 | 2,663 |
| 47 | Axiomatic Attribution for Deep Networks Mukund Sundararajan, Ankur Taly, Qiqi Yan · arXiv (Cornell University) | 4 Mar 2017 | 2,648 |
| 48 | Striving for Simplicity: The All Convolutional Net Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox et al. · arXiv (Cornell University) | 21 Dec 2014 | 2,593 |
| 49 | Channel Pruning for Accelerating Very Deep Neural Networks Yihui He, Xiangyu Zhang, Jian Zhong Sun | Oct 2017 | 2,587 |
| 50 | What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? Alex Kendall, Yarin Gal · arXiv (Cornell University) | 15 Mar 2017 | 2,533 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.