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: 2040209
• 负责人: Matthew Wright
• 金额: $ 38.58万
• 依托单位: Rochester Institute of Tech
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040209&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作品，并开始被用来破坏政府和选举。即使是deepfakes的威胁也让人们对新闻中视频的真实性产生了怀疑。记者在核实信息方面发挥着关键作用，他们尤其需要帮助来应对不断改进的deepfake技术。最近在检测deepfake方面的结果很有希望，在实验室测试中的准确率接近100%，但很少有系统可用于现实世界。我们的目标是开发DeFake工具，这是一个利用先进的机器学习技术帮助记者以一种稳健、直观的方式检测deepfake的系统，并提供可向公众解释的结果。为了实现这一目标，项目团队主要负责四项任务：（1）使该工具对新类型的deepfake具有鲁棒性，并向用户展示为什么视频是假的;（2）保护该工具免受对抗性示例的影响-对视频的小干扰，这些干扰是专门为欺骗检测系统而设计的;（3）与记者合作，了解他们需要从工具中获得什么，并建立一个在线社区，讨论deepfake及其检测;（4）将其他工作的进展整合成一个稳定、有效和有用的工具，并积极向记者传播这一工具。该项目团队还利用视觉上有趣的deepfake来开发吸引人的教育和推广工作，例如针对所有年龄段的广泛观众的博物馆式deepfake检测展览。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Evaluating Robustness of Sequence-based Deepfake Detector Models by Adversarial Perturbation

• DOI: 10.1145/3494109.3527194
• 发表时间: 2022-05
• 期刊: Proceedings of the 1st Workshop on Security Implications of Deepfakes and Cheapfakes
• 作者: S. A. Shahriyar;M. Wright

Gradient Frequency Modulation for Visually Explaining Video Understanding Models

用于视觉解释视频理解模型的梯度频率调制

• 发表时间: 2021
• 期刊: British Machine Vision Conference
• 作者: Lin, X;Bao, W;Wright, M;Kong, Y
• 通讯作者: Kong, Y