通用對抗擾動攻擊

Risk Description

Attackers craft input-agnostic universal adversarial perturbations that can be added to any input to cause model misclassification. Unlike sample-specific adversarial attacks, universal perturbations can be applied at scale — for example, a single universal patch placed on any traffic sign can deceive autonomous vehicle recognition systems. This dramatically lowers attack barriers and costs.