metadata

license: mit
language:
  - en
pretty_name: W-Bench
size: 10,000 instances

What is it?

W-Bench is the first holistic benchmark that incorporates four types of image editing techniques to assess the robustness of watermarking methods. 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.

Dataset Structure

The evaluation set is divided into 6 different categories:

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)
1,000 samples for image-to-video generation
1,000 samples for testing conventional distortion

How to download and use 🍷 W-Bench

Using `huggingface_hub`

from huggingface_hub import snapshot_download
folder = snapshot_download(
"Shilin-LU/W-Bench",
repo_type="dataset",
local_dir="./W-Bench/",
allow_patterns="DET_INVERSION_1K/image/*")

For faster downloads, make sure to install pip install huggingface_hub[hf_transfer] and set the environment variable HF_HUB_ENABLE_HF_TRANSFER=1.

Using `datasets`

from datasets import load_dataset
wbench = load_dataset("Shilin-LU/W-Bench", streaming=True)

Citation Information

Paper on arXiv