Collaborative Research: SaTC: TTP: Small: DeFake: Deploying a Tool for Robust Deepfake Detection

协作研究：SaTC：TTP：小型：DeFake：部署强大的 Deepfake 检测工具

• 批准号: 2310131
• 负责人: Andrea Hickerson
• 金额: $ 11.42万
• 依托单位: University of Mississippi
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310131&HistoricalAwards=false
• 关键词: Collaborative; Research; SaTC; TTP; Small

Deepfakes – videos that are generated or manipulated by artificial intelligence – pose a major threat for spreading disinformation, threatening blackmail, and new forms of phishing. They are already widely used in creating non-consensual pornography, and have begun to be used to undermine governments and elections. Even the threat of deepfakes has cast doubts on the authenticity of videos in the news. Journalists, who have a key role in verifying information, especially need help to deal with ever-improving deepfake technology. Recent results on detecting deepfakes are promising, with close to 100% accuracy in lab tests, but few systems are available for real-world use. It is critical to move beyond accuracy on curated datasets and address the needs of journalists who could benefit from these advances.The objective of this transition-to-practice project is to develop the DeFake tool, a system that utilizes advanced machine learning to help journalists detect deepfakes in a way that is robust, intuitive, and provides results that are explainable to the general public. To meet this objective, the project team is engaged in four main tasks: (1) Making the tool robust to new types of deepfakes, and having it show users why a video is fake; (2) Protecting the tool from adversarial examples – small perturbations to a video that are specially crafted to fool detection systems; (3) Working with journalists to understand what they need from the tool, and building an online community to discuss deepfakes and their detection; and (4) Integrating advances from the other tasks into a stable, efficient, and useful tool, and actively disseminating this tool to journalists. The project team is also leveraging visually interesting deepfakes to develop engaging education and outreach efforts, such as a museum-style exhibit on deepfake detection meant for broad audiences of all ages.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Deepfakes——由人工智能生成或操纵的视频——对传播虚假信息、威胁性勒索和新形式的网络钓鱼构成了重大威胁。它们已经被广泛用于制作未经同意的色情内容，并开始被用来破坏政府。甚至深度造假的威胁也让人们对新闻中视频的真实性产生了怀疑，而记者在验证信息方面发挥着关键作用，尤其需要帮助来应对不断改进的深度造假技术的检测结果。 Deepfakes 很有前途，在实验室测试中准确率接近 100%，但很少有系统可用于现实世界，超越策划数据集的准确性并满足可以从这些进步中受益的记者的需求至关重要。这个过渡到实践项目的重点是开发 DeFake 工具，该系统利用先进的机器学习帮助记者以稳健、直观的方式检测深度伪造，并提供可向公众解释的结果来满足这一要求。目标、项目团队致力于四项主要任务：(1) 使该工具能够应对新型深度伪造，并向用户展示视频为何是假的；(2) 保护该工具免受对抗性示例的影响——对特定视频的小干扰；旨在愚弄检测系统；(3) 与记者合作，了解他们对该工具的需求，并建立一个在线社区来讨论深度造假及其检测；(4) 将其他任务的进展集成到稳定、高效和可靠的系统中；有用的工具，并积极项目团队还利用视觉上有趣的深度伪造品来开展引人入胜的教育和宣传活动，例如面向所有年龄段的广大观众举办关于深度伪造品检测的博物馆式展览。该奖项反映了 NSF 的法定使命，并已得到认可。通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。