Moderating Illicit Online Image Promotion for Unsafe User-Generated Content Games Using Large Vision-Language Models
arXiv:2403.18957v3 Announce Type: replace-cross 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 compr
The proliferation of user-generated content games (UGCGs) and the demonstrated capabilities of Large Vision-Language Models (LVLMs) are converging, making content moderation solutions both urgent and feasible.
This development addresses a critical online safety concern, particularly for children, by leveraging advanced AI to combat illicit content, thereby impacting regulatory pressure and platform responsibility.
AI-driven content moderation for visual illicit material within online games will become more sophisticated and automated, shifting the burden from manual review and enhancing platform security.
- · Online gaming platforms
- · Social media companies
- · AI developers (LVLMs)
- · Children and adolescents
- · Perpetrators of illicit content promotion
- · Manual content moderation services
Increased safety and reduced exposure to harmful content for young users in online games.
Heightened demand for cutting-edge AI moderation tools and services across all user-generated content platforms.
Potential for LVLMs to be integrated into real-time, preventative content filtering, reducing illicit material before broadcast.
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