  Deepfakes, as AI-generated media, have increasingly threatened media
integrity and personal privacy with realistic yet fake digital content. In this
work, we introduce an open-source and user-friendly online platform,
DeepFake-O-Meter v2.0, that integrates state-of-the-art methods for detecting
Deepfake images, videos, and audio. Built upon DeepFake-O-Meter v1.0, we have
made significant upgrades and improvements in platform architecture design,
including user interaction, detector integration, job balancing, and security
management. The platform aims to offer everyday users a convenient service for
analyzing DeepFake media using multiple state-of-the-art detection algorithms.
It ensures secure and private delivery of the analysis results. Furthermore, it
serves as an evaluation and benchmarking platform for researchers in digital
media forensics to compare the performance of multiple algorithms on the same
input. We have also conducted detailed usage analysis based on the collected
data to gain deeper insights into our platform's statistics. This involves
analyzing two-month trends in user activity and evaluating the processing
efficiency of each detector.
