Adversarial Robustness for Machine Learning | The MLSecOps Podcast скачать в хорошем качестве

Adversarial Robustness for Machine Learning | The MLSecOps Podcast

The MLSecOps Podcast | Season 1 Episode 3 With Guest Pin-Yu Chen, PhD In this episode of The MLSecOps podcast, the co-hosts interview Pin-Yu Chen, Principal Research Scientist at IBM Research, about his book co-authored with Cho-Jui Hsieh, "Adversarial Robustness for Machine Learning." Chen explores the vulnerabilities of machine learning (ML) models to adversarial attacks and provides examples of how to enhance their robustness. The discussion delves into the difference between Trustworthy AI and Trustworthy ML, as well as the concept of LLM practical attacks, which take into account the practical constraints of an attacker. Chen also discusses security measures that can be taken to protect ML systems and emphasizes the importance of considering the entire model lifecycle in terms of security. Finally, the conversation concludes with a discussion on how businesses can justify the cost and value of implementing adversarial defense methods in their ML systems. Thanks for listening! Find more episodes and read the transcript at: https://bit.ly/MLSecOpsPodcast. Additional MLSecOps and AI Security tools and resources to check out: Protect AI Radar (https://bit.ly/ProtectAIRadar) ModelScan (https://bit.ly/ModelScan) Protect AI’s ML Security-Focused Open Source Tools (https://bit.ly/ProtectAIGitHub) Huntr - The World's First AI/Machine Learning Bug Bounty Platform (https://bit.ly/aimlhuntr)