Attacking machine learning with adversarial examples

OpenAI Blog · 9 years ago

Adversarial examples are inputs deliberately designed to trick machine learning models into producing incorrect outputs, functioning as optical illusions for AI systems. Researchers have demonstrated these attacks work across multiple input types, including images, audio, and text modalities. The difficulty in defending against such attacks stems from the fundamental challenge of making machine learning systems robust to intentionally crafted deceptive inputs that humans would recognize correctly.