Adversarial Training
Adversarial training involves augmenting the training data with 'adversarial examples' specifically crafted by an adversary to mislead the model. The model is then trained on this mixed dataset, learning to correctly classify both original and perturbed inputs. This process aims to make the model more robust and resilient against malicious attacks that try to cause misclassifications with subtle input changes.
It's like a boxing coach training their fighter to withstand sneaky, unexpected punches by practicing against a deceptive sparring partner.
It improves the security and reliability of AI systems, especially in sensitive applications like self-driving cars or medical diagnosis.
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