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GCC AI Research

Adversarial Training: Improvements and Applications

MBZUAI · Notable

Research Ethics ML NLP Security

Summary

This article discusses adversarial training (AT) as a method to improve the robustness of machine learning models against adversarial attacks. AT aims to correctly classify data and ensure no data fall near decision boundaries, simulating adversarial attacks during training. Dr. Jingfeng Zhang from RIKEN-AIP will present on improvements to AT and its application in evaluating and enhancing the reliability of ML methods. Why it matters: As ML models become more prevalent in real-world applications in the GCC region, ensuring their robustness against adversarial attacks is crucial for maintaining their reliability and security.

Keywords

adversarial training · machine learning · RIKEN-AIP · robustness · adversarial attacks

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