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cvlab-columbia · Mar 3, 2023 · a5d173b · a5d173b
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-# Understandingg and Benchmarking Zero-Shot Adversarial Robustness for Foundation Models
+# Understanding and Benchmarking Zero-Shot Adversarial Robustness for Foundation Models
 
-## CLIP Model
+Pretrained large-scale vision-language models like CLIP have exhibited strong generalization over unseen tasks, yet imperceptible adversarial perturbations can significantly reduce their performance. Standard 
+
+
+## Zero-Shot Adversarial Robustness Challenge
 
+| Defence Method 	| Submitted By    	| Accuracy<br>(Robust) | Accuracy<br>(Clean) 	  | Submission Date 	|
+|----------------	|-----------------	|----------------	|-----------------	|-----------------	|
+|       TeCoA with Finetuning        | (initial entry) 	|      **38.18%**  |         55.97%     |      Mar 1, 2023        |
+|   Vanilla CLIP-B/32 (no defence)  | (initial entry) 	|      6.57        |     **64.56%**     |      Mar 1, 2023        |
 
 
+## CLIP Model
+
 For adapting for zero-shot adversarial robustness with visual prompting, run
 
 `python visual_prompt.py`