VModA: An Effective Framework for Adaptive NSFW Image Moderation

TL;DR

VModA is a versatile framework that significantly improves the accuracy and adaptability of NSFW image detection across diverse content types and regulations, addressing current method limitations.

Contribution

The paper introduces VModA, a novel adaptive framework that enhances NSFW detection accuracy and handles complex semantics and varying moderation rules.

Findings

Achieves up to 54.3% accuracy improvement over existing methods.

Demonstrates strong adaptability across categories, scenarios, and models.

Re-annotated and corrected datasets, improving benchmark quality.

Abstract

Not Safe/Suitable for Work (NSFW) content is rampant on social networks and poses serious harm to citizens, especially minors. Current detection methods mainly rely on deep learning-based image recognition and classification. However, NSFW images are now presented in increasingly sophisticated ways, often using image details and complex semantics to obscure their true nature or attract more views. Although still understandable to humans, these images often evade existing detection methods, posing a significant threat. Further complicating the issue, varying regulations across platforms and regions create additional challenges for effective moderation, leading to detection bias and reduced accuracy. To address this, we propose VModA, a general and effective framework that adapts to diverse moderation rules and handles complex, semantically rich NSFW content across categories. Experimental results show that VModA significantly outperforms existing methods, achieving up to a 54.3% accuracy improvement across NSFW types, including those with complex semantics. Further experiments demonstrate that our method exhibits strong adaptability across categories, scenarios, and base VLMs. We also identified inconsistent and controversial label samples in public NSFW benchmark datasets, re-annotated them, and submitted corrections to the original maintainers. Two datasets have confirmed the updates so far. Additionally, we evaluate VModA in real-world scenarios to demonstrate its practical effectiveness.