Automated Face Selection and Censoring on Image and Video using Artificial Intelligent

The Personal Data Protection Act (PDPA) was created to prevent the breach of personal information of users of computer systems without the data owner's consent. One type of data that frequently has problems with privacy violations is images and videos. Because of difficult control, as a result, there are often extraneous people in the frame instead of just the intended subject. If the person caught in the frame does not want this information published, there will be a problem with that video. This causes the identity of the person to be concealed so that they can be identified. Although doing this is an acceptable method, censorship is labor-intensive and time-consuming. For these reasons, we proposed the Automated Face Selection and Censoring on Image and Video System using Multi-Task Cascaded Convolutional Neural Networks (MTCNN) Model to automatically detect the face and censor only unwanted persons. Furthermore, our proposed method also executes an automated system that can sense and ignore some frames that are not essential or redundant with other nearby frames to reduce complex processing and time-consumption.