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Adversarial examples: A survey

2018 Baltic URSI Symposium (URSI), 2018
Adversarial examples are a phenomenon that have gathered a lot of attention in recent studies. The fact that the addition of very small, but carefully crafted perturbations to the inputs of sophisticated and high performing machine learning models may cause them to make significant errors, is both fascinating and important.
openaire   +1 more source

Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey

IEEE Communications Surveys and Tutorials, 2023
Ke He, Dong Seong Kim, Muhammad Rizwan Asghar   +2 more
exaly  

Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary Survey

IEEE Communications Surveys and Tutorials, 2023
Yulong Wang, Xin Yuan, Wei Ni
exaly  

A Survey of Adversarial Attack and Defense Methods for Malware Classification in Cyber Security

IEEE Communications Surveys and Tutorials, 2023
Senming Yan, Jing Ren, Wei Wang
exaly  

A Survey on Generative Adversarial Networks: Variants, Applications, and Training

ACM Computing Surveys, 2022
Songyuan Li
exaly  

Generative Adversarial Networks

ACM Computing Surveys, 2022
Zhipeng Cai, Honghui Xu, Yi Pan
exaly  

Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain

ACM Computing Surveys, 2022
Ishai Rosenberg, Asaf Shabtai
exaly  

Adversarial Machine Learning: A Multilayer Review of the State-of-the-Art and Challenges for Wireless and Mobile Systems

IEEE Communications Surveys and Tutorials, 2022
Jinxin Liu, Michele Nogueira, Burak Kantarci   +2 more
exaly  

Adversarial Machine Learning in Wireless Communications Using RF Data: A Review

IEEE Communications Surveys and Tutorials, 2023
Damilola Adesina, Yalin E Sagduyu, Lijun Qian   +2 more
exaly  

A Survey of Adversarial Defenses and Robustness in NLP

ACM Computing Surveys, 2023
Shreya Goyal, Sumanth Doddapaneni, Mitesh Khapra   +2 more
exaly