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    Adversarial Machine Learning Challenges
    Adversarial Machine Learning 2019-02-21 Summary from the abstract
    Adversarial Machine Learning and Cybersecurity
    Micah Musser

    Artificial intelligence systems are rapidly being deployed in all sectors of the economy, yet significant research has demonstrated that these systems can be vulnerable to a wide array of attacks. How different are these problems from more common cybersecurity vulnerabilities? …

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    Artificial intelligence systems are rapidly being deployed in all sectors of the economy, yet significant research has demonstrated that these systems can be vulnerable to a wide array of attacks. How different are these problems from more common cybersecurity vulnerabilities? What legal ambiguities do they create, and how can organizations ameliorate them? This report, produced in collaboration with the Program on Geopolitics, Technology, and Governance at the Stanford Cyber Policy Center, presents the recommendations of a July 2022 workshop of experts to help answer these questions.

    2023-04-01 Full text summarized
    Adversarial Machine Learning
    Aneesh Sreevallabh Chivukula, Xinghao Yang, Bo Liu, Wei Liu, Wanlei Zhou
    Adversarial Machine Learning 2022-08-26 Summary from the abstract
    Adversarial robustness in meta-learning and contrastive learning
    Pin-Yu Chen, Cho-Jui Hsieh
    Adversarial Robustness for Machine Learning 2023-01-01 Summary from the abstract
    References
    Adversarial Machine Learning 2019-02-21 Summary from the abstract
    Glossary
    Adversarial Machine Learning 2019-02-21 Summary from the abstract
    Introduction
    Adversarial Machine Learning 2019-02-21 Summary from the abstract
    Index
    Adversarial Machine Learning 2019-02-21 Summary from the abstract
    A Framework for Secure Learning
    Adversarial Machine Learning 2019-02-21 Summary from the abstract
    Copyright
    Adversarial Robustness for Machine Learning 2023-01-01 Summary from the abstract