Sample-wise Targeted Adversarial Attacks on Test-time Adaptation
The work introduces a sample-wise targeted attack designed to exploit Test-time Adaptation (TTA) by misclassifying only inputs containing a specific trigger while keeping benign predictions consistent. It uses meta-learning to generate perturbations that generalize well, and employs a priority-aware gradient alignment strategy to optimize the attack despite conflicting objectives.