Research on Sensitive Information Recognition Algorithm Based on Deep Neural Network - Huang, Shuaina; Dmitry Semyonovich, Karpovich (International Journal of Computer Science and Information Technology, 2026)
The spread of pornographic, violent, and politically sensitive images on social networks poses serious risks to youth well-being and social stability, making accurate detection essential for public safety. However, existing models often compromise representational capacity to meet computational constraints, while challenges such as illumination variation, changes in sensitive-region scale, and background interference further limit recognition accuracy. To address these issues, this study proposes a lightweight sensitive-image recognition framework. A high-order convolutional module is introduced to extract fine-grained features and suppress irrelevant background noise. A feature-guided fusion module is further designed to integrate texture and deep semantic features, improving robustness against lighting fluctuations and noise. The overall architecture builds on a compact high-resolution network enhanced with depthwise separable convolutions, MBConv layers, and Ghost Modules to significantly reduce parameters while maintaining strong performance. Experiments on a multi-class sensitive-image dataset verify the model’s efficiency and effectiveness.