Moderating Illicit Online Image Promotion for Unsafe User Generated Content Games Using Large Vision-Language Models

Authors: 

Keyan Guo, Ayush Utkarsh, Wenbo Ding, and Isabelle Ondracek, University at Buffalo; Ziming Zhao, Northeastern University; Guo Freeman, Clemson University; Nishant Vishwamitra, The University of Texas at San Antonio; Hongxin Hu, University at Buffalo

Abstract: 

Online user generated content games (UGCGs) are increasingly popular among children and adolescents for social interaction and more creative online entertainment. However, they pose a heightened risk of exposure to explicit content, raising growing concerns for the online safety of children and adolescents. Despite these concerns, few studies have addressed the issue of illicit image-based promotions of unsafe UGCGs on social media, which can inadvertently attract young users. This challenge arises from the difficulty of obtaining comprehensive training data for UGCG images and the unique nature of these images, which differ from traditional unsafe content. In this work, we take the first step towards studying the threat of illicit promotions of unsafe UGCGs. We collect a real-world dataset comprising 2,924 images that display diverse sexually explicit and violent content used to promote UGCGs by their game creators. Our in-depth studies reveal a new understanding of this problem and the urgent need for automatically flagging illicit UGCG promotions. We additionally create a cutting-edge system, UGCG-Guard, designed to aid social media platforms in effectively identifying images used for illicit UGCG promotions. This system leverages recently introduced large vision-language models~(VLMs) and employs a novel conditional prompting strategy for zero-shot domain adaptation, along with chain-of-thought (CoT) reasoning for contextual identification. UGCG-Guardachieves outstanding results, with an accuracy rate of 94% in detecting these images used for the illicit promotion of such games in real-world scenarios.

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BibTeX
@inproceedings {299571,
author = {Keyan Guo and Ayush Utkarsh and Wenbo Ding and Isabelle Ondracek and Ziming Zhao and Guo Freeman and Nishant Vishwamitra and Hongxin Hu},
title = {Moderating Illicit Online Image Promotion for Unsafe User Generated Content Games Using Large {Vision-Language} Models},
booktitle = {33rd USENIX Security Symposium (USENIX Security 24)},
year = {2024},
isbn = {978-1-939133-44-1},
address = {Philadelphia, PA},
pages = {5787--5804},
url = {https://www.usenix.org/conference/usenixsecurity24/presentation/guo-keyan},
publisher = {USENIX Association},
month = aug
}
Guo Appendix PDF