PFI–TT: Development of an Explainable and Robust Detector of Forged Multimedia and Cyber Threats using Artificial intelligence

PFI™TT：使用人工智能开发可解释且强大的伪造多媒体和网络威胁检测器

• 批准号: 2329858
• 负责人: Khalid Malik
• 金额: $ 54.92万
• 依托单位: Oakland University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2329858&HistoricalAwards=false
• 关键词: PFI; TT; Development; Explainable; Robust

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is in defending those falsely accused with fake multimedia evidence, and prevent individuals from deflecting legitimate accusations based on genuine evidence. The system developed will prevent cyber threats such as impersonation, fraud, social engineering attacks, and the submission of falsified evidence, thus safeguarding individuals, organizations, and the legal system. The proposed project has the potential to protect against cyber harassment, privacy invasion, and prevent the dissemination of false information by identifying harmful or misleading audio and video deepfake content. It will have a significant impact in a number of areas, including the media, politics, business, and social networks, by maintaining the integrity of digital multimedia. If digital forgeries are left unchecked, trust in the courts, the media and the government will necessarily erode, and as deepfakes and other forged multimedia become more prevalent it will become imperative to have a trustworthy source of truth to assist these entities with the identification of fakes. Finally, the project has the potential to drive innovation in artificial intelligence (AI) and computer vision, leading to the development of more robust deepfake detection methods.The proposed project attacks the rising problem of forged multimedia in the legal system by offering the development of a trustworthy, AI-based, Deep Forgery Detector (DFD). It has a sound foundation in Neuro-symbolic AI, combining deep models with a symbolic approach in order to enable abstracting, reasoning, and explainability. The DFD will also enable single and multimodal data authenticity analysis to identify any tampering, or manipulation, such as fully or partially AI generated content. Additionally, the DFD’s hybrid nature, with its anomaly-detection and signature-based approach, will help to detect both known and unknown forgeries, further improving its generalizability. The report generated by the DFD will outline the underlying facts used for determination, using text and visual evidence, and will provide an authoritative answer by analyzing both the visual and audio aspects at file- and frame-levels. This project will leverage attack-resistant algorithms, improving their capabilities through iterative development, enabling the DFD to capture traces of anti-forensic processing and making it an attack-aware detector driven by game-theoretic and decoy mechanisms. Lastly, the DFD will also offer the users a context-aware interaction through a dialog/chatbot feature in order to elaborate its decision-making process and personalize the contents of the generated report.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.

这种伙伴关系对创新 - 技术翻译（PFI -TT）项目的更广泛的影响/商业潜力在于捍卫那些错误地被指控伪造多媒体证据的人，并防止个人根据真实证据表现出合法的指控。开发的系统将防止诸如模仿，欺诈，社会工程攻击以及提交虚假证据之类的网络威胁，从而保护个人，组织和法律制度。拟议的项目有可能通过识别有害或误导的音频和视频深击内容来防止网络骚扰，隐私入侵并防止虚假信息传播。通过保持数字多媒体的完整性，它将在包括媒体，政治，商业和社交网络在内的许多领域产生重大影响。如果将数字伪造者不受限制，对法院的信任，媒体和政府将一定会侵蚀，并且随着深击和其他伪造的多媒体变得更加普遍，必须拥有一个值得信赖的真理来源，以帮助这些实体确定宠物。最后，该项目有潜力推动人工智能（AI）和计算机视觉的创新，从而发展了更强大的深层检测方法。拟议的项目通过提供可信赖的，基于AI的深层伪造检测器（DFD）来开发法律体系中被遗忘的多媒体的上升问题。它在神经符号AI中具有声音基础，将深层模型与象征性方法相结合，以实现抽象，推理和解释性。 DFD还将启用单模式数据真实性分析，以确定任何篡改或操纵，例如完全或部分AI生成的内容。此外，DFD的混合性质具有异常检测和基于签名的方法，将有助于检测已知和未知的伪造，从而进一步提高其普遍性。 DFD生成的报告将概述用于确定，文本和视觉证据的基本事实，并通过分析文件和帧级别的视觉和音频方面来提供权威答案。该项目将利用抗攻击算法，通过迭代开发提高其功能，使DFD能够捕获抗飞质处理的痕迹，并使之成为受游戏理论和诱饵机制驱动的攻击感检测器。最后，DFD还将通过对话框/聊天机器人功能为用户提供背景感知的互动，以详细阐述其决策过程并个性化生成的报告的内容。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛的影响审查审查的Criteria来通过评估来通过评估来获得的支持。