Adversarial Examples
Adversarial examples are inputs to a machine learning model that an attacker has intentionally designed to cause the model to make a mistake. These inputs are often perturbed (slightly altered) in a way that is imperceptible to humans but drastically changes the model's prediction. They highlight vulnerabilities in AI systems, demonstrating that models can be fragile even when performing well on clean, unperturbed data.
It's like a magician creating an optical illusion that fools your brain into seeing something that isn't truly there, even though your eyes are working perfectly.
Recognizing and mitigating adversarial examples is critical for building secure and reliable AI systems, especially in sensitive applications like self-driving cars or medical diagnoses.
Learn one new AI thing every day.
Daily Deck sends you seven plain-English cards like this every morning. Free.
Start free