mlsanityData to 14 Oct 2026

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

arXiv 2404.13146 ↗First posted 19 Apr 2024cs.CR cs.CVcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 41 of its 61 references that are in the map. First posted on arXiv 19 Apr 2024.

Builds on

41 of 61 references.

References · 41

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29 more
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Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024Community Forensics: Using Thousands of Generators to Train Fake Image Detectorscited by 50
  2. 2024Towards a Universal Synthetic Video Detector: From Face or Background Manipulations to Fully AI-Generated Contentcited by 22
  3. 2025SynthGuard: An Open Platform for Detecting AI-Generated Multimedia with Multimodal LLMscited by 0