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The increasing transmission of illegal videos over the Internet imposes the needs to develop large-scale digital video forensics systems for prosecuting and deterring digital crimes in the Internet. In this paper, we propose, design, and implement a novel large-scale Digital Forensics Service Platform (DFSP) that can effectively detect illegal content from Internet videos. More specifically, we propose a distributed architecture by taking advantage of Content Delivery Network (CDN) to improve scalability, which can process enormous number of Internet videos in real time. We propose CDN-based Resource-Aware Scheduling (CRAS) algorithm, which schedules the tasks efficiently in the DFSP according to resource parameters, such as delay and computation load. We deploy the DFSP system in the Internet, which integrates the CDN-based distributed architecture and CRAS algorithm with a large-scale video detection algorithm, and evaluate the deployed system. Our evaluation results demonstrate the effectiveness of the platform.