Update README.md

README.md

@@ -6,12 +6,15 @@ pretty_name: W-Bench
 size: 10,000 instances
 ---
 
+# **[ICLR 2025]** [Robust Watermarking Using Generative Priors Against Image Editing: From Benchmarking to Advances](https://arxiv.org/abs/2410.18775)
+
 # What is it?
+
 **W-Bench is the first benchmark to evaluate watermarking robustness across four image editing techniques.** 
 
 Eleven representative watermarking methods are evaluated on the W-Bench. The W-Bench contains 10,000 images sourced from datasets such as COCO, Flickr, ShareGPT4V, etc. 
 
-GitHub Page: [https://github.com/Shilin-LU/VINE](https://github.com/Shilin-LU/VINE)
+GitHub Repo: [https://github.com/Shilin-LU/VINE](https://github.com/Shilin-LU/VINE)
 
 # Dataset Structure
 
@@ -19,7 +22,7 @@ The evaluation set consists of six subsets, each targeting a different type of A
 - 1,000 samples for stochastic regeneration
 - 1,000 samples for deterministic regeneration
 - 1,000 samples for global editing
-- 5,000 samples for local editing (divided into five sets, each containing 1,000 images, with different mask sizes ranging from 10–60% of the image area)
+- 5,000 samples for local editing (divided into five sets, each containing 1,000 images and 1,000 masks, with different mask sizes ranging from 10–60% of the image area)
 - 1,000 samples for image-to-video generation
 - 1,000 samples for testing conventional distortion
 
@@ -27,6 +30,12 @@ The evaluation set consists of six subsets, each targeting a different type of A
 
 ## Using `huggingface_hub`
 
+```
+huggingface-cli download Shilin-LU/W-Bench --repo-type=dataset --local-dir W-Bench
+```
+
+or
+
 ```python
 from huggingface_hub import snapshot_download
 folder = snapshot_download(