Majority of the techniques currently employed for detecting illegal videos were primarily based on video content analysis, which in turn was computationally intensive and cannot be applied for real-time detection of large video traffic flow. To address the said issue, the novel approach of real-time video source identification was proposed in this paper for preventing the transmission of illegal videos. In contrast to conventional approached based on content analysis, the proposed approach blocked the transmission of potentially illegal videos by detecting the camera sources that had previously produced illegal videos. If a video call was performed from a cellphone that had previously produced illegal videos, a security warning was issued for blocking the video transmission. The proposed approach was based on the assumption that video streams were more likely to be illegal if they have been produced from a camera that was used earlier for distributing illegal videos. Hence, the primary objective of the proposed approach was to develop a real-time and reliable method for camera source identification. To achieve this two objective, the following three aspects were taken into consideration. Firstly, a database comprising hundred videos was established for evaluation. The videos in the database were obtained from twenty-five cellphones of various brands and models, wherein each video indicated the corresponding camera source. Secondly, a simplified and feasible method was developed for real-time video source identification. Finally, a novel feature decision-making model with multiple integrated fingerprint features was incorporated for enhancing the reliability of video source identification. Obtained experimental results indicate that the proposed approach for camera source identification can meet real-time requirements and is significantly superior to conventional approaches. Furthermore, in the case of Android cellphones, the accuracy of camera source identification is found to be 98.161%, which corroborates the feasibility of the proposed approach.