用视觉语言模型检测安卓游戏内容评级违规，准确率超现有方法6%。
Detecting Content Rating Violations in Android Applications: A Vision-Language Approach
- 融合图文信息的多模态模型预测游戏内容评级
- 在真实场景中发现70多起评级违规，9个带教师认证标签
- 识别出违规应用的下架率比正常应用高7个百分点
尽管监管机构已建立可靠的移动应用内容评级指南，但谷歌应用商店中的内容评级仍由开发者自行申报。由于数据量庞大且需同时解析文本与图像内容并关联至评级标准，人工审核困难，自动化检测也面临挑战。本文提出一种基于视觉-语言的多模态方法，用于预测安卓游戏类应用的内容评级并检测违规行为，使用了流行安卓游戏的元数据数据集。在多模态设置下，该方法相比当前最优的CLIP微调模型相对准确率提升约6%。在实际应用中，检测出超过70起可能的内容评级违规案例，其中包括9个带有“教师认证”徽章的应用。此外，被分类为违规的应用有34.5%已被移除，而正确分类应用的移除率为27%，表明本模型能有效识别出更可能因用户投诉被下架的应用。
原文摘要 · Abstract (English)
Despite regulatory efforts to establish reliable content-rating guidelines for mobile apps, the process of assigning content ratings in the Google Play Store remains self-regulated by the app developers. There is no straightforward method of verifying developer-assigned content ratings manually due to the overwhelming scale or automatically due to the challenging problem of interpreting textual and visual data and correlating them with content ratings. We propose and evaluate a visionlanguage approach to predict the content ratings of mobile game applications and detect content rating violations, using a dataset of metadata of popular Android games. Our method achieves ~6% better relative accuracy compared to the state-of-the-art CLIP-fine-tuned model in a multi-modal setting. Applying our classifier in the wild, we detected more than 70 possible cases of content rating violations, including nine instances with the 'Teacher Approved' badge. Additionally, our findings indicate that 34.5% of the apps identified by our classifier as violating content ratings were removed from the Play Store. In contrast, the removal rate for correctly classified apps was only 27%. This discrepancy highlights the practical effectiveness of our classifier in identifying apps that are likely to be removed based on user complaints.
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