基于两阶段算法的多媒体有害信息识别方法

Multimedia Harmful Information Recognition Method Based on Two-stage Algorithm

在互联网安全监管和网络违法犯罪打击整治的应用场景中,现有多媒体有害信息识别方法普遍存在运算效率不高、无法准确识别局部敏感信息,以及识别检测局限于单一的网络违法犯罪类型等问题。针对以上问题,文中提出了一种基于两阶段算法的多媒体有害信息识别模型。该模型将信息过滤与内容检测分阶段处理,将场景识别和元素目标检测分任务并行处理,第一阶段采用EfficientNet-B2构建高吞吐的前置过滤模块快速筛选掉80%正常内容的数据;第二阶段基于Meal-V2,Faster RCNN,NetVLAD网络构建3种不同网络结构的模块,适应多维度场景、多特征元素的识别要求。结果表明,模型运算效率在T4卡上达到57 FPS,多媒体有害信息的识别准确率、召回率均超过97%;与传统模型相比,在NPDI和自建测试集上识别准确率分别最高提升3.09%和19.26%。

In the application scenarios of Internet content security supervision and combating and rectifying Internet crimes,existing multimedia harmful information identification methods generally have problems such as low computational efficiency,inability to accurately identify local sensitive information,and identification capabilities are limited to a single type of cyber crimes.In order to solve the above problems,the paper proposes a multimedia harmful information recognition model based on a two-stage algorithm.This method processes information filtering and content detection in stages,and splits the tasks of scene recognition and element target detection.The first stage uses EfficientNet-B2 to build a high-throughput pre-filter model to quickly filter out 80%of images and short videos with normal content.In the second stage,three modules with different network structures are built based on Meal-V2,Faster RCNN,and NetVLAD networks to adapt to the recognition requirements of multi-dimensional scenes and multi-feature elements.The results show that the model’s computing efficiency reaches 57FPS(frames per second)on the T4 card,and the recognition accuracy and recall rate of multimedia harmful information exceed 97%.Compared with traditional mo-dels,the recognition accuracy rate on the NPDI dataset and the self-built test dataset increases by 3.09%and 19.26%respectively.