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.

为了防止将非法视频发布到Internet上并造成不良影响，视频站点需要手动查看每个新发布的视频。手动审核既费时又费力，并且容易遗漏。在这种背景下，本文旨在提出一种自动检测视频中非法内容的方法。自动视频检测可以大大减少审核员的工作并提高检测效率。本研究提出了一种基于模糊支持向量机（FSVM）的多模式融合特征违背视频检测方法。首先，提取实时视频的多种模式特征，包括静止图像特征，运动特征和音频特征。其次，使用FSVM对各种形态特征数据进行分类，得到不同形态下的分类结果。最后，将不同模式下的分类结果合并以获得最终决策结果。这项研究的创新之处在于，引入多种模态特征丰富了样本信息，使样本信息更加全面。这很容易区分。分类器FSVM基于传统的SVM，为每个样本分配隶属度，从而减少孤立点和噪声对最佳决策面的影响。实验表明，该研究提高了非法视频的检测效率，可以满足实际应用的要求。使样本信息更全面。这很容易区分。分类器FSVM基于传统的SVM，为每个样本分配隶属度，从而减少孤立点和噪声对最佳决策面的影响。实验表明，该研究提高了非法视频的检测效率，可以满足实际应用的要求。使样本信息更全面。这很容易区分。分类器FSVM基于传统的SVM，为每个样本分配隶属度，从而减少了孤立点和噪声对最佳决策面的影响。实验表明，该研究提高了非法视频的检测效率，可以满足实际应用的要求。