A Real-Time Detection Framework for High-Risk Content on Short Video Platforms Based on Heterogeneous Feature Fusion

This paper presents a novel real-time detection framework for high-risk content on short video platforms based on heterogeneous feature fusion. Social media platforms continuously face challenges in identifying harmful content across multimodal data streams including video, audio, and text. We propose an adaptive fusion architecture that dynamically integrates features from multiple modalities, coupled with efficient processing mechanisms to enable real-time operation. The framework implements a three-stage fusion process with cross-modal attention mechanisms to emphasize discriminative features across modalities. Experimental evaluation using a dataset of 25,000 video samples across five risk categories demonstrates that our approach achieves 95.3% accuracy, 94.8% precision, and 94.2% recall, outperforming state-of-the-art baselines by 4.8% on average. The system maintains an average processing latency of 46ms per content item through adaptive caching and pipeline processing techniques. Ablation studies reveal that cross-modal attention and adaptive fusion components contribute most significantly to performance improvements. Feature importance analysis identifies semantic content from text, speech content from audio, and motion patterns from video as the most discriminative features for high-risk content detection. The framework demonstrates strong generalization capability with 91.7% accuracy on out-of-distribution samples, providing valuable insights for implementing large-scale content moderation systems.