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Generative Adversarial Networks in Time Series: A Systematic Literature Review
ACM Computing Surveys, 2023Eoin Brophy, Zhengwei Wang, Qi She
exaly
Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain
ACM Computing Surveys, 2022Ishai Rosenberg, Asaf Shabtai
exaly
A Survey on Adversarial Recommender Systems
ACM Computing Surveys, 2022Yashar Deldjoo, Tommaso Di Noia
exaly
Defense Mechanisms Against Adversarial Attacks
Adversarial attacks are particularly cybersecurity applications where reliability and accuracy are the most important. They are a significant threat to artificial intelligence systems (AI). These attacks involve subtle manipulation of input data developed into deceptive AI models that lead to false output or system dusk.openaire +1 more source
Improving Defense Against Intelligent Adversaries
2012This 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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How Generative Adversarial Networks and Their Variants Work
ACM Computing Surveys, 2020, Uiwon Hwang, Sungroh Yoon
exaly