Adversarial Examples

Adversarial examples are inputs designed to trick machine learning models, like image recognition systems. For instance, an image might look normal to a human but includes subtle alterations that confuse the model, causing it to misclassify the image. This highlights vulnerabilities in AI systems, exposing how small, intentional changes can lead to incorrect outputs, similar to how an optical illusion can deceive the eye. Understanding adversarial examples is crucial for improving AI robustness and ensuring reliable decision-making in applications like self-driving cars or facial recognition.