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，在线虚假信息的令人震惊且令人不安。虚假信息的一种令人不安的形式是操纵图像/音频/视频以模仿别人。现实的操作可以由先进的AI技术以深层神经网络的形式产生，并且通常被称为深击。可以将深击武器武器造成负面后果。尽管检测方法在基准数据集上证明了有希望的性能，但它们不足，并且有几个局限性。该项目旨在通过主动和积极主动的方法更有效地对抗深层效果，以扎根深层，并保护个人免受深层攻击。在产生深层之前，主动和主动的方法生效。积极的方法不会干扰训练或产生深泡，而主动方法则旨在破坏这些过程以防止深层捕捞。这项项目的工作提供了及时和必要的技术，以减轻整个网络空间和社会中深层影响的负面影响。该项目包括四项主要的研究活动。首先是加强对当前的深层检测方法的防御，以防止抗飞行攻击。这里采用的方法是使用随机的集合模型，该模型与无限分类模型合奏中随机选择的成员进行处理。第二个活动旨在将深层捕获归因于其生成模型，即恢复创建深层的特定方式。此步骤是重要的追踪深瓦的起源和作者。在第三项活动中，研究工作集中在通过污染训练数据来积极添加痕迹的方法上。 该项目的第四个活动进一步研究可以通过使用数据中毒破坏训练过程来阻碍产生深层泡沫的方法。中毒的数据将导致效率降低和低质量的深层效果。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。