Multi-Label Classification and Sensitive Area Location Model for Illegal Images for Network Content Review

Illegal image identification within network content review systems requires robust solutions capable of handling both complex multi-label classification and precise localization of sensitive regions. This study presents an integrated framework designed to enhance the accuracy and interpretability of automated moderation by combining a multi-label classification model with advanced spatial analysis. Central to this framework is the Sensitive Area Detection Network (SADN), which leverages convolutional feature extraction and multi-head attention to capture diverse semantic cues while generating refined bounding box predictions for regions requiring human-level scrutiny. SADN adopts a dual output structure that jointly optimizes label prediction and spatial localization, enabling comprehensive understanding of content containing multiple forms of illegal material. Complementing SADN is the Adaptive Content Review Strategy (ACRS), a dynamic mechanism that incorporates multimodal information, adaptive learning, and a scalable verification pipeline. ACRS integrates visual and textual cues through a fusion-based encoder, applies attention-guided refinement to highlight high risk regions, and adopts reinforcement-driven updates to improve decision making across evolving content types. Experimental evaluations conducted on multiple datasets demonstrate that the proposed framework significantly improves classification accuracy, reduces false positives, and enhances localization precision compared with state-of-the-art approaches. Together, SADN and ACRS provide a unified and scalable foundation for next generation content review systems, offering both high detection performance and interpretable spatial outputs essential for real-world moderation workflows.