LAUSR

In order to prevent the illegal videos from being posted on the Internet and causing adverse effects, video sites need to manually review each newly released video. The manual review is time-consuming and labor-intensive, and is prone to omissions. Against this background, this article intends to propose a method for automatically detecting illegal content in videos. Automatic video detection can greatly reduce the work of auditors and improve detection efficiency. This study proposes a multi-modal fusion feature violation video detection method using fuzzy support vector machine (FSVM). First, extract multiple modal features of live video, including still image features, motion features, and audio features. Secondly, FSVM is used to classify the feature data of various modalities to obtain the classification results under different modalities. Finally, the classification results in different modes are merged to obtain the final decision result. The innovation of this study is that the introduction of multiple modal features enriches the sample information, making the sample information more comprehensive. Which is easy to distinguish. The classifier FSVM is based on the traditional SVM to assign a degree of membership to each sample, thereby reducing the impact of isolated points and noise on the optimal decision surface. Experiments show that this study improves the detection efficiency of illegal videos and can meet the requirements of practical applications.