Attacking machine learning with adversarial examples

📰 OpenAI News

Adversarial examples are inputs designed to cause machine learning models to make mistakes, and securing systems against them can be challenging

intermediate Published 24 Feb 2017

Action Steps

Understand how adversarial examples are created
Recognize the types of attacks that can be launched using adversarial examples
Develop strategies to secure machine learning models against adversarial attacks

Who Needs to Know This

Machine learning engineers and security teams benefit from understanding adversarial examples to improve model robustness and security

Key Insight

💡 Adversarial examples are a significant security threat to machine learning models