Keyword

adversarial attacks

Description: Deliberately crafted inputs designed to cause machine-learning models to make incorrect predictions, studied in areas such as computer vision and medical imaging.

Primary topic: Adversarial Robustness in Machine Learning (66%)

Related topic: Adversarial Robustness in Machine Learning (66%)

Wikidata ID: wiki/Q115727242

OpenAlex ID: keywords/adversarial-attacks

Towards Evaluating the Robustness of Neural Networks

2017 · Nicholas Carlini, David Wagner · Proceedings - IEEE Symposium on Security and Privacy/Proceedings of the ... IEEE Symposium on Security and Privacy · 8,836

DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks

2016 · Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 5,311

On Assessing ML Model Robustness: A Methodological Framework (Academic Track)

2025 · Awadid, Afef, Robert, Boris · OASIcs : OpenAccess Series in Informatics · 4,723

Towards Deep Learning Models Resistant to Adversarial Attacks

2017 · Madry, Aleksander, Makelov, Aleksandar, et al. · arXiv (Cornell University) · 4,069

The Limitations of Deep Learning in Adversarial Settings

2016 · Nicolas Papernot, Patrick McDaniel, et al. · IEEE European Symposium on Security and Privacy · 3,985

Boosting Adversarial Attacks with Momentum

2018 · Yinpeng Dong, Fangzhou Liao, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 3,140

Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks

2016 · Nicolas Papernot, Patrick McDaniel, et al. · Proceedings - IEEE Symposium on Security and Privacy/Proceedings of the ... IEEE Symposium on Security and Privacy · 3,115

Universal Adversarial Perturbations

2017 · Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2,675

Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey

2018 · Naveed Akhtar, Ajmal Mian · IEEE Access · 2,020

Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks

2018 · Weilin Xu, David Evans, et al. · Network and Distributed System Security Symposium · 1,856

Adversarial Examples: Attacks and Defenses for Deep Learning

2019 · Xiaoyong Yuan, Pan He, et al. · IEEE Transactions on Neural Networks and Learning Systems · 1,728

Accessorize to a Crime

2016 · Mahmood Sharif, Sruti Bhagavatula, et al. · ACM SIGSAC Conference on Computer and Communications Security (CCS) · 1,526

Secure Estimation and Control for Cyber-Physical Systems Under Adversarial Attacks

2014 · Hamza Fawzi, Paulo Tabuada, et al. · IEEE Transactions on Automatic Control · 1,284

Adversarial attacks on medical machine learning

2019 · Samuel G. Finlayson, John D. Bowers, et al. · Science · 1,081

Adversarial Attacks on Neural Networks for Graph Data

2018 · Daniel Zügner, Amir Akbarnejad, et al. · ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) · 977

Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

2020 · Croce, Francesco, Hein, Matthias · arXiv (Cornell University) · 965

Generating Adversarial Examples with Adversarial Networks

2018 · Chaowei Xiao, Bo Li, et al. · International Joint Conference on Artificial Intelligence · 940

Feature Denoising for Improving Adversarial Robustness

2019 · Cihang Xie, Yuxin Wu, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 937

Proceedings of the 29th ACM International Conference on Multimedia

2021 · Yue, Jiutao, Li, Haofeng, et al. · 922

Square Attack: A Query-Efficient Black-Box Adversarial Attack via Random Search

2020 · Maksym Andriushchenko, Francesco Croce, et al. · Lecture notes in computer science · 877

Can machine learning be secure?

2006 · Marco Barreno, Blaine Nelson, et al. · ACM Symposium on Information, Computer and Communications Security · 850

Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models

2018 · Samangouei, Pouya, Kabkab, Maya, et al. · arXiv (Cornell University) · 839

Adversarial learning

2005 · Daniel Lowd, Christopher Meek · 755

Inverting Gradients -- How easy is it to break privacy in federated learning?

2020 · Geiping, Jonas, Bauermeister, Hartmut, et al. · arXiv (Cornell University) · 749

Extracting Training Data from Large Language Models

2020 · Carlini, Nicholas, Tramer, Florian, et al. · arXiv (Cornell University) · 727

Works count: 20,760

Citation count: 255,700