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Improving Defense Against Intelligent Adversaries

2012
This is the first of four chapters devoted to public-sector applications of risk analysis and possible ways to improve them. The applications we consider are defending against attacks by terrorists or other intelligent adversaries (this chapter), assessing and promoting food safety (next chapter), and assessing the public health benefits and fairness ...
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A Survey of Adversarial Defenses and Robustness in NLP

ACM Computing Surveys, 2023
Shreya Goyal   +2 more
exaly  

Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and the Way Forward

IEEE Communications Surveys and Tutorials, 2020
Adnan Qayyum   +2 more
exaly  

Generative Adversarial Networks in Computer Vision

ACM Computing Surveys, 2022
Zhengwei Wang, Tomas E Ward
exaly  

Generative Adversarial Networks (GANs)

ACM Computing Surveys, 2022
Divya Saxena, Jiannong Cao
exaly  

Generative Adversarial Networks in Time Series: A Systematic Literature Review

ACM Computing Surveys, 2023
Eoin Brophy, Zhengwei Wang, Qi She
exaly  

Adversarial Attacks and Defenses in Deep Learning: From a Perspective of Cybersecurity

ACM Computing Surveys, 2023
Shuai Zhou, Chi Liu, Dayong Ye
exaly  

Robust Defense Against Adversarial Attacks with Defensive Preprocessing and Adversarial Training

2025 IEEE International Conference on Consumer Electronics (ICCE)
Chih-Yang Lin   +4 more
openaire   +1 more source

Interpreting Adversarial Examples in Deep Learning: A Review

ACM Computing Surveys, 2023
Sicong Han, Chenhao Lin, Chao Shen
exaly  

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