Attacking machine learning with adversarial examples
Analysis
The article introduces adversarial examples, highlighting their nature as intentionally designed inputs that mislead machine learning models. It promises to explain how these examples function across various platforms and the challenges in securing systems against them. The focus is on the vulnerability of machine learning models to carefully crafted inputs.
Key Takeaways
Reference
“Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they’re like optical illusions for machines.”