Sensitive Information Detection Based on Deep Learning Models

Abstract

Currently, a large number of pornographic images on the Internet severely affect the growth of adolescents. In order to create a healthy and benign online environment, it is necessary to recognize and detect these sensitive images. Current techniques for detecting pornographic content are still in an immature stage, with the key issue being low detection accuracy. To address this problem, this paper proposes a method for detecting pornographic content based on an improved YOLOv8 model. Firstly, InceptionNeXt is introduced into the backbone network to enhance the model’s adaptability to images of different scales and complexities by optimizing feature extraction through parallel branches and deep convolution. Simultaneously, the SPPF module is simplified into the SimCSPSPPF module, which further enhances the effectiveness and diversity of features through improved spatial pyramid pooling and cross-layer feature fusion. Secondly, switchable dilated convolutions are incorporated to improve the adaptability of the C2f enhancement model and enhance the model’s detection capability. Finally, SEAattention is introduced to enhance the model’s ability to capture spatial details. The experiments demonstrate that the model achieves an mAP@0.5 of 79.7% on our self-made sensitive image dataset, which is a significant improvement of 5.9% compared to the previous YOLOv8n network. The proposed method excels in handling complex backgrounds, targets of varying scales, and resource-constrained scenarios, while simultaneously improving the model’s computational efficiency without compromising detection accuracy, making it more advantageous for practical applications.