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.

我们介绍了一个开源且用户友好的在线平台，DeepFake-O-Meter v2.0，该平台整合了用于检测 Deepfake 图像、视频和音频的最先进方法。该平台以 DeepFake-O-Meter v1.0 为基础，在平台架构设计方面进行了重大升级和改进，包括用户交互、检测器整合、工作平衡和安全管理。该平台旨在为日常用户提供方便的服务，使用多种最先进的检测算法分析 DeepFake 媒体，确保分析结果的安全和私密传递。此外，它还作为一个评估和基准平台，供数字媒体取证研究人员比较多个算法在相同输入上的性能。我们还根据收集的数据进行了详细的使用分析，以深入了解我们平台的统计信息。这包括分析用户活动的两个月趋势和评估每个检测器的处理效率。

DeepFake-O-Meter v2.0：一个用于 DeepFake 检测的开放平台

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

AI-generated media has become a threat to our digital society as we know it.
These forgeries can be created automatically and on a large scale based on
publicly available technology. Recognizing this challenge, academics and
practitioners have proposed a multitude of automatic detection strategies to
detect such artificial media. However, in contrast to these technical advances,
the human perception of generated media has not been thoroughly studied yet.
In this paper, we aim at closing this research gap. We perform the first
comprehensive survey into people's ability to detect generated media, spanning
three countries (USA, Germany, and China) with 3,002 participants across audio,
image, and text media. Our results indicate that state-of-the-art forgeries are
almost indistinguishable from "real" media, with the majority of participants
simply guessing when asked to rate them as human- or machine-generated. In
addition, AI-generated media receive is voted more human like across all media
types and all countries. To further understand which factors influence people's
ability to detect generated media, we include personal variables, chosen based
on a literature review in the domains of deepfake and fake news research. In a
regression analysis, we found that generalized trust, cognitive reflection, and
self-reported familiarity with deepfakes significantly influence participant's
decision across all media categories.

AI 生成的媒体对我们所知的数字社会构成威胁。与技术进步相比，人们对生成媒体的感知尚未得到彻底研究。本文通过针对音频、图像和文本媒体跨三个国家（美国、德国和中国）的 3,002 位参与者进行全面的调查，结果显示，最先进的赝品几乎无法与 “真实” 媒体区分，大多数参与者在被要求评价时只是猜测它们是人工生成的还是机器生成的。此外，AI 生成的媒体在所有媒体类型和所有国家中更容易被误认为是人类产生的。为了进一步了解影响人们对生成媒体检测能力的因素，本文根据对 deepfake 和虚假新闻研究领域的文献综述，包括个人变量。通过回归分析，我们发现广义信任、认知反思和自报熟悉程度等个人变量显著影响参与者在所有媒体类别中的决策。

跨国人工生成媒体的人类检测代表性研究

A Representative Study on Human Detection of Artificially Generated  Media Across Countries

Deepfakes are AI-generated media in which an image or video has been
digitally modified. The advancements made in deepfake technology have led to
privacy and security issues. Most deepfake detection techniques rely on the
detection of a single modality. Existing methods for audio-visual detection do
not always surpass that of the analysis based on single modalities. Therefore,
this paper proposes an audio-visual-based method for deepfake detection, which
integrates fine-grained deepfake identification with binary classification. We
categorize the samples into four types by combining labels specific to each
single modality. This method enhances the detection under intra-domain and
cross-domain testing.

深度伪造是通过人工智能生成的媒体，其中图像或视频经过数字修改。本研究提出了一种基于音视频的深度伪造检测方法，将细粒度的深度伪造识别与二元分类结合，增强了在域内和跨域测试下的检测能力。