Adversarial training has made strides in improving model robustness, yet it still falls short of providing a comprehensive defense against attacks. While classifiers can achieve high accuracy in benign conditions, their performance plummets to near zero under adversarial attacks. Despite advancements, the current methods only offer partial protection, highlighting the ongoing challenges in ensuring security in machine learning applications.
