Bernard Asante

Augmentation-Guided Adversarial Training via Local Stability-Based Sample Selection

Adversarial training remains one of the most effective defenses against adversarial attacks, but its standard PGD-based training is computationally expensive because adversarial examples are generated for every training sample. Selective adversarial training reduces this cost by perturbing only a subset of samples. How...