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.
Abstract (translated)
深度伪造（Deepfakes）作为人工智能生成的媒体， increasingly 威胁到媒体诚信和个人隐私，因为它们具有真实但虚假的数字内容。在这项工作中，我们介绍了一个开源且用户友好的在线平台 DeepFake-O-Meter v2.0，它整合了最先进的方法来检测 Deepfake 图像、视频和音频。基于 DeepFake-O-Meter v1.0，我们在架构设计方面进行了显著的升级和改进，包括用户交互、检测器集成、工作平衡和安全管理。该平台旨在为用户提供一个方便的 Deepfake 媒体分析服务，使用多种最先进的技术。它确保了分析结果的安全和隐私交付。此外，它还成为数字媒体法医研究人员比较多种算法在相同输入上的性能的平台。我们根据收集到的数据进行了详细的使用分析，以更深入地了解我们平台的统计数据。这包括分析两个月内的用户活动趋势和评估每个检测器的处理效率。
URL
https://arxiv.org/abs/2404.13146
