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

arXiv: https://arxiv.org/abs/2403.18957 · HTML: https://mlsanity.com/paper/2403.18957/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 74 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation](https://mlsanity.com/paper/2204.11788/) (2022)
- [Chain of Thought Prompt Tuning in Vision Language Models](https://mlsanity.com/paper/2304.07919/) (2023)
- [Chain of Explanation: New Prompting Method to Generate Higher Quality Natural Language Explanation for Implicit Hate Speech](https://mlsanity.com/paper/2209.04889/) (2022)
- [Moderating New Waves of Online Hate with Chain-of-Thought Reasoning in Large Language Models](https://mlsanity.com/paper/2312.15099/) (2023)
- [Disturbed YouTube for Kids: Characterizing and Detecting Inappropriate Videos Targeting Young Children](https://mlsanity.com/paper/1901.07046/) (2019)
- [A Trade-off-centered Framework of Content Moderation](https://mlsanity.com/paper/2206.03450/) (2022)

## Built on by (most cited first, 14 of 14)

- [UnsafeBench: Benchmarking Image Safety Classifiers on Real-World and AI-Generated Images](https://mlsanity.com/paper/2405.03486/) (2024)
- [MLLM-as-a-Judge for Image Safety without Human Labeling](https://mlsanity.com/paper/2501.00192/) (2024)
- [Detecting Visual Information Manipulation Attacks in Augmented Reality: A Multimodal Semantic Reasoning Approach](https://mlsanity.com/paper/2507.20356/) (2025)
- [When Memory Becomes a Vulnerability: Towards Multi-turn Jailbreak Attacks against Text-to-Image Generation Systems](https://mlsanity.com/paper/2504.20376/) (2025)
- [DREAM: Scalable Red Teaming for Text-to-Image Generative Systems via Distribution Modeling](https://mlsanity.com/paper/2507.16329/) (2025)
- [Bridging the Gap in Vision Language Models in Identifying Unsafe Concepts Across Modalities](https://mlsanity.com/paper/2507.11155/) (2025)
- [BLM-Guard: Explainable Multimodal Ad Moderation with Chain-of-Thought and Policy-Aligned Rewards](https://mlsanity.com/paper/2602.18193/) (2026)
- [Code Vulnerability Repair with Large Language Model using Context-Aware Prompt Tuning](https://mlsanity.com/paper/2409.18395/) (2024)
- [Gen-AI for User Safety: A Survey](https://mlsanity.com/paper/2411.06606/) (2024)
- [VModA: An Effective Framework for Adaptive NSFW Image Moderation](https://mlsanity.com/paper/2505.23386/) (2025)
- [Malicious Image Analysis via Vision-Language Segmentation Fusion: Detection, Element, and Location in One-shot](https://mlsanity.com/paper/2512.04599/) (2025)
- [Are Open-Weight LLMs Ready for Social Media Moderation? A Comparative Study on Bluesky](https://mlsanity.com/paper/2602.05189/) (2026)
- [Detection of Illicit Content on Online Marketplaces using Large Language Models](https://mlsanity.com/paper/2603.04707/) (2026)
- [When MLLMs miss what humans read: diagnosing and learning to read squint text](https://mlsanity.com/paper/2610.13465/) (2026)

## Abstract (arXiv)

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-Guard achieves outstanding results, with an accuracy rate of 94% in detecting these images used for the illicit promotion of such games in real-world scenarios.