“Real Attackers Don't Compute Gradients”: Bridging the Gap Between Adversarial ML Research and Practice

Author:

Apruzzese Giovanni1,Anderson Hyrum S.2,Dambra Savino3,Freeman David4,Pierazzi Fabio5,Roundy Kevin3

Affiliation:

1. University of Liechtenstein

2. Robust Intelligence

3. Norton Research Group

4. Meta

5. King's College London

Publisher

IEEE

Cited by 25 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally;IEEE Transactions on Artificial Intelligence;2024-09

2. “Hey Players, there is a problem…”: On Attribute Inference Attacks against Videogamers;2024 IEEE Conference on Games (CoG);2024-08-05

3. A Survey on Malware Detection with Graph Representation Learning;ACM Computing Surveys;2024-06-29

4. WikiPhish: A Diverse Wikipedia-Based Dataset for Phishing Website Detection: Data/Toolset Paper;Proceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy;2024-06-19

5. "Are Adversarial Phishing Webpages a Threat in Reality?" Understanding the Users' Perception of Adversarial Webpages;Proceedings of the ACM Web Conference 2024;2024-05-13

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