Projected gradient descent (PGD) attack
The projected gradient descent (PGD) attack is a widely used adversarial method in machine learning that iteratively perturbs input data to fool neural networks while ensuring perturbations remain within a specified constraint. It is a cornerstone technique for testing model robustness against adversarial examples.