AI RESEARCH
ATAC: Augmentation-Based Test-Time Adversarial Correction for CLIP
arXiv CS.CV
•
ArXi:2511.17362v3 Announce Type: replace Despite its remarkable success in zero-shot image-text matching, CLIP remains highly vulnerable to adversarial perturbations on images. As adversarial fine-tuning is prohibitively costly, recent works explore various test-time defense strategies; however, these approaches still exhibit limited robustness. In this work, we revisit this problem and propose a simple yet effective strategy: Augmentation-based Test-time Adversarial Correction