With the advent and popularization of Internet mass media, massive data have been generated in forms of texts, images, videos, etc. This poses a serious challenge for the review of related content, especially the security review of the image data. At present, the safety analysis of image data is not mature and thus criminals often hack websites and tamper with the images on the websites, which poses significant threats to network security. Targeted at this practical application, this study designs and implements a web image data security analysis system, which is composed of two major modules:(1) illegal image detection engine module based on deep learning algorithm; (2) image tamper-proof module based on event trigger technology and plug-in polling technology. The system can quickly review whether the image data content is legitimate and automatically monitor whether the image data has been tampered with.