Deepfake检测困境：合成媒体中对抗动态的多利益相关者探索
The Deepfake Detection Dilemma: A Multistakeholder Exploration of Adversarial Dynamics in Synthetic Media
摘要：
合成媒体检测技术将媒体标记为合成或非合成，并且越来越多地被记者、网络平台和公众用于识别错误信息和其他形式的有问题内容。随着资源丰富的组织和非技术性的公众产生更复杂的合成媒体，有问题内容的提供者适应的能力引发了\newterm{detection dilemma}：随着检测方法变得更容易获得，它们变得更容易规避。本文介绍了来自学术界、技术平台、媒体实体和民间社会组织的多利益相关者群体如何积极参与合成媒体检测及其社会技术影响，评估检测困境。具体而言，我们提供了对检测环境和对手能力的评估，这些能力来自更广泛的全球人工智能和媒体诚信社区，这些社区关注如何减轻有害合成媒体的传播。人物角色的集合说明了在他们的技术能力背景下，不成熟和资源丰富的错误信息赞助商之间的交集。这项工作的结论是，有没有“最好的”方法来导航检测器的困境，但从多利益相关者的输入，以更好地通知检测过程中的决策和政策，在实践中得出一组的影响。
英文摘要：
Synthetic media detection technologies label media as either synthetic or non-synthetic and are increasingly used by journalists, web platforms, and the general public to identify misinformation and other forms of problematic content. As both well-resourced organizations and the non-technical general public generate more sophisticated synthetic media, the capacity for purveyors of problematic content to adapt induces a \newterm{detection dilemma}: as detection practices become more accessible, they become more easily circumvented. This paper describes how a multistakeholder cohort from academia, technology platforms, media entities, and civil society organizations active in synthetic media detection and its socio-technical implications evaluates the detection dilemma. Specifically, we offer an assessment of detection contexts and adversary capacities sourced from the broader, global AI and media integrity community concerned with mitigating the spread of harmful synthetic media. A collection of personas illustrates the intersection between unsophisticated and highly-resourced sponsors of misinformation in the context of their technical capacities. This work concludes that there is no "best" approach to navigating the detector dilemma, but derives a set of implications from multistakeholder input to better inform detection process decisions and policies, in practice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
