How do adversarial attacks on machine learning models work, and what strategies can be employed to defend against them?

Adversarial attacks involve creating inputs that are intentionally designed to deceive machine learning models, causing them to make incorrect predictions. These attacks exploit vulnerabilities in the model’s training data or architecture. Defense strategies include adversarial training (augmenting the training set with adversarial examples), defensive distillation (reducing the model’s sensitivity to small perturbations), and using robust optimization techniques to improve the model’s resilience to adversarial inputs.