DeepFake-O-Meter v2.0: An Open Platform for DeepFake Detection

TL;DR

DeepFake-O-Meter v2.0 is an open-source platform that enables users to detect and analyze Deepfake media using multiple advanced algorithms, while also serving as a benchmarking tool for researchers.

Contribution

We developed an upgraded, user-friendly online platform that integrates multiple state-of-the-art Deepfake detection methods and provides a benchmarking environment for digital media forensics.

Findings

Increased user engagement over two months

Enhanced detection accuracy with integrated algorithms

Improved processing efficiency for various detectors

Abstract

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.