A Closer Look at "Adversarial Robustness for Machine Learning" With Guest: Pin-Yu Chen

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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 checking out the MLSecOps Podcast! Get involved with the MLSecOps Community and find more resources at https://community.mlsecops.com.
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#Tech #MLSecOps.com #Machine Learning #Artificial Intelligence #Cybersecurity #MLSecOps #Ai Security #InfoSec #Generative AI #Llm #ChatGPT #Responsible Ai #CISO

A Closer Look at "Adversarial Robustness for Machine Learning" With Guest: Pin-Yu Chen

The MLSecOps Podcast

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Recon: Automated Red Teaming for GenAI

Protect AI’s ML Security-Focused Open Source Tools

LLM Guard Open Source Security Toolkit for LLM Interactions

Huntr - The World's First AI/Machine Learning Bug Bounty Platform

53 פרקים

#Tech #MLSecOps.com #Machine Learning #Artificial Intelligence #Cybersecurity #MLSecOps #Ai Security #InfoSec #Generative AI #Llm #ChatGPT #Responsible Ai #CISO

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The MLSecOps Podcast « » A Closer Look at "Adversarial Robustness for Machine Learning" With Guest: Pin-Yu Chen

A Closer Look at "Adversarial Robustness for Machine Learning" With Guest: Pin-Yu Chen

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A Closer Look at "Adversarial Robustness for Machine Learning" With Guest: Pin-Yu Chen