Please be aware that this dataset contains unsafe images. The ground truth labels are labeled according to whether the sample contains unsafe content. "1" represents unsafe, "0" refers to safe.

Remember to email us before you send the request to acknowledge your personal information, affiliation, and research plan (and don't forget your HuggingFace account).

By sending us an access request, you MUST agree to the following terms and conditions:

Uphold the privacy of users in this dataset by not attempting to re-identify them.
Not distribute the data to others.
In exchange for access to this dataset, you will properly cite the following papers when publishing using this dataset:

Guo, K., Utkarsh, A., Ding W., Ondracek, I., Zhao, Z., Freeman, G., Vishwamitra, N., & Hu, H. (2024, August). Moderating Illicit Online Image Promotion for Unsafe User Generated Content Games Using Large Vision-Language Models. In 33rd USENIX Security Symposium (USENIX Security 24) USENIX Association.

Bibtex:

@article{Guo2024ModeratingIO,
  title={{Moderating Illicit Online Image Promotion for Unsafe User Generated Content Games Using Large Vision-Language Models}},
  author={Keyan Guo and Ayush Utkarsh and Wenbo Ding and Isabelle Ondracek and Ziming Zhao and Guo Freeman and Nishant Vishwamitra and Hongxin Hu},
  booktitle = {{USENIX Security Symposium (USENIX Security)}},
  publisher = {USENIX},
  year = {2024}
}

If you have any questions regarding this dataset, please email Keyan at keyanguo@buffalo.edu.

More details about this work can be found in our GitHub repo.

Datasets:

keyan96
/

unsafe-ugcg-images

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license: apache-2.0