Pornographic video detection based on semantic and image enhancement

Pornographic video detection is of significant importance in curbing the proliferation of pornographic information on online video platforms. However, existing works often employ generic frame extraction methods that ignore the low-latency requirements of detection scenarios and the characteristics of pornographic videos. Additionally, existing detection methods have difficulties in detail characterization and semantic understanding, resulting in low accuracy. Therefore, this paper proposes an efficient pornographic video detection framework based on semantic and image enhancement. Firstly, a keyframe extraction method tailored for pornographic video detection is proposed to select representative frames. Secondly, a light enhancement method is introduced to facilitate accurate capture of pornographic visual cues. Moreover, a compression-reconstruction network is employed to eliminate adversarial perturbations, enabling models to obtain reliable features. Subsequently, YOLOv5 is introduced to locate and crop human targets in keyframes, reducing background interference and enhancing the expression of human semantic information. Finally, MobileNetV3 is employed to determine if the human targets contain pornographic content. The proposed framework is validated on the publicly available NPDI dataset, achieving an accuracy of 95.9%, surpassing existing baseline methods.