What are adversarial attacks in the context of machine learning, and how can they be mitigated?

Adversarial attacks are malicious inputs intentionally designed to deceive machine learning models, causing them to make incorrect predictions or classifications. These attacks exploit vulnerabilities in the model’s decision boundaries. Techniques to mitigate adversarial attacks include robust training methods, adversarial training, and input preprocessing.