SaTC: CORE: Small: Combating AI Synthesized Media Beyond Detection

SaTC：核心：小型：对抗无法检测的人工智能合成媒体

• 批准号: 2153112
• 负责人: Siwei Lyu
• 金额: $ 49.9万
• 依托单位: SUNY at Buffalo
• 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 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2153112&HistoricalAwards=false
• 关键词: SaTC; CORE; Small; Combating; AI

Recent years have seen a startling and troubling rise of online disinformation. One disconcerting form of disinformation is the manipulation of images/audios/videos to impersonate someone else. Realistic manipulations can be generated by advanced AI technologies in the form of deep neural networks, and commonly known as deepfakes. Deepfakes can be weaponized to cause negative consequences. Although detection methods have demonstrated promising performance on benchmark datasets, they are not adequate and have several limitations. This project aims to combat deepfakes more effectively and beyond detection with active and proactive approaches to root out deepfakes and protect individuals from deepfake attacks. The active and proactive approaches take effect before the deepfake is generated. The active approach does not interfere with the training or generation of deepfake, whereas the proactive approach aims to disrupt these processes to prevent the deepfake. This project work provides timely and needed technologies to mitigate the negative impacts of deepfakes in cyberspace and society at large.This project includes four main research activities. The first is to strengthen the defense of current deepfake detection methods against anti-forensic attacks. The approach taken here is to use random ensemble models that process input with a randomly chosen member from an infinite ensemble of classification models. The second activity aims to attribute a deepfake to its generation model, i.e., recover the specific means that a deepfake is created. This step is important tracing a deepfake's origin and author. In the third activity, the research effort is focused on the methods that actively add traces to synthesized deepfakes by contaminating the training data. The fourth activity of this project further studies methods that can obstruct deepfake generation by using data poisoning to sabotage the training process. The poisoned data will lead to reduced efficiency and low-quality deepfakes.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.

近年来，网上虚假信息的数量惊人且令人不安。虚假信息的一种令人不安的形式是操纵图像/音频/视频来冒充他人。逼真的操作可以由先进的人工智能技术以深度神经网络的形式生成，通常被称为deepfakes。Deepfakes可以被武器化，造成负面后果。虽然检测方法在基准数据集上表现出有前途的性能，但它们并不足够，并且具有一些限制。该项目旨在更有效地打击deepfake，并通过积极主动的方法来根除deepfake并保护个人免受deepfake攻击。主动和积极的方法在deepfake生成之前生效。主动方法不会干扰deepfake的训练或生成，而主动方法旨在破坏这些过程以防止deepfake。该项目工作提供及时和必要的技术，以减轻deepfakes在网络空间和整个社会的负面影响。该项目包括四个主要研究活动。一是加强当前deepfake检测方法对反取证攻击的防御。这里采用的方法是使用随机集成模型，该模型使用从分类模型的无限集成中随机选择的成员来处理输入。第二个活动旨在将deepfake归因于其生成模型，即，recover是创建deepfake的具体方式。这一步对于追踪Deepfake的起源和作者非常重要。在第三项活动中，研究工作的重点是通过污染训练数据来主动向合成的deepfake添加痕迹的方法。 该项目的第四项活动进一步研究了通过使用数据中毒来破坏训练过程来阻止deepfake生成的方法。中毒的数据将导致效率降低和低质量的deepfakes。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。