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Adversarial Machine Learning for Text
Proceedings of the Sixth International Workshop on Security and Privacy Analytics, 2020In this tutorial, we investigate the history, evolution and latest research topics in the area of adversarial machine learning for text data. Both classical attacks on spam filters and more recent attacks on deep learning models for text classification problems will be discussed. We then discuss proposed and potential defenses against these attacks. We
Daniel Lee, Rakesh Verma
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Machine learning in adversarial environments
Machine Learning, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Laskov, Pavel, Lippmann, Richard
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Adversarial Machine Learning and Explainability
2021Do you see any difference between these two pandas? I bet the answer is no; we don’t have any doubt on saying that both of them represent a panda. But as shown by Goodfellow et al. (2014), the first one has been classified as a panda by a NN with 55.7% confidence, while the second has been classified by the same NN as a gibbon with 99.3% confidence ...
Leonida Gianfagna, Antonio Di Cecco
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A state-of-the-art review on adversarial machine learning in image classification
Multimedia tools and applications, 2023Ashish Bajaj, D. Vishwakarma
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Defense strategies for Adversarial Machine Learning: A survey
Computer Science Review, 2023Panagiotis Bountakas +3 more
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Adversarial Machine Learning in Cybersecurity
With the evolution and penetration of AI and ML into almost all critical public life domains including cybersecurity, the cybercrime ecosystem attempts to tap the vulnerabilities in AI-based cybersecurity systems by invoking adversarial machine learning, which has posed a significant challenge to cyber-physical security systems employing ML.Vikram Singh, Sanyogita Singh
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Adversarial Machine Learning in Cybersecurity
2019Adversarial machine learning algorithms deal with adversarial sample generation which is creating false input data that are capable enough to fool any machine learning model. For instance, attributes of a goodware can be added to a malware executable to make the classifier identify a malicious sample as benign.
Tony Thomas +2 more
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Poltergeist: Acoustic Adversarial Machine Learning against Cameras and Computer Vision
IEEE Symposium on Security and Privacy, 2021Xiaoyu Ji +6 more
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Adversarial Machine Learning in Wireless Communications Using RF Data: A Review
IEEE Communications Surveys and Tutorials, 2023Damilola Adesina +2 more
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