Collaborative Research: SaTC: CORE: Medium: Self-Learning and Self-Evolving Detection of Altered, Deceptive Images and Videos

伪造和欺骗性的图像和视频，不仅吸引人的眼睛真实的，但也愚弄现有的计算机程序，现在可以产生先进的人工智能技术，俗称“deepfake”技术。恶意方可以利用新技术将受害者的脸换成不舒服或虚构的场景，并损害此人的声誉。Deepfake技术可能被用来制造虚假新闻，影响竞选活动的结果，在金融市场制造混乱，用虚假的灾难场景愚弄公众，或煽动公共暴力和增加国家之间的冲突。该项目的目标是设计一种智能深度伪造检测器，能够评估数字视觉内容的完整性，并自动检测真实的伪造图像或视频，并防止它们传播。这项研究的成功将为数十亿社交网络用户提供一个更值得信赖和健康的环境，并确保数字取证的视觉内容的真实性，从而使我们的社会受益。 该项目团队由两名研究人员组成，他们在图像处理和网络安全方面具有互补的专业知识。该项目将大大推进伪造视觉内容检测的最新技术水平。该系统的独特之处在于其自学习和自进化的能力，以捕获随着时间的推移由目前未知的deepfake算法生成的改变和欺骗性视觉内容。所提出的自进化机制将允许深度伪造检测器快速适应仅具有少量样本的新型伪造图像或视频，克服现有数据饥饿学习算法中有限样本的限制。拟议的防御机制将确保deepfake检测器的鲁棒性，并防止其将隐藏或模糊的伪造视觉内容错误分类为真实内容。该项目将解决虚假内容检测问题，并减轻机器学习中现有的未解决的对抗性攻击。拟议的终身学习机制将使deepfake检测器能够利用积累的知识，随着时间的推移实现自我改进。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。

Forged and deceptive images and videos that not only appeal real to human eyes but also fool existing computer programs can now be generated by advanced artificial intelligent techniques, colloquially called "deepfake" techniques. Malicious parties can utilize the new techniques to swap a victim's face into uncomfortable or fictional scenes and damage that person's reputation. Deepfake techniques may be exploited to create false news, to affect results in election campaigns, to create chaos in financial markets, to fool the public with false disaster scenes, or to inflame public violence and increase conflict between nations. The objective of this project is to design an intelligent deepfake detector that will be capable of assessing the integrity of digital visual content and automatically detect falsified images or videos in real time and prevent them from spreading. The success of the proposed research will benefit our society by providing a more trustworthy and healthy environment for billions of social network users and ensuring the authenticity of visual content for digital forensics. The project team consists of two researchers with complementary expertise in image processing and cybersecurity. The project will significantly advance the state of the art in falsified visual content detection. The uniqueness of the proposed system is its ability of self-learning and self-evolving to capture altered and deceptive visual content generated by currently unknown deepfake algorithms over time. The proposed self-evolving mechanisms will allow a deepfake detector to quickly adapt to new types of forged images or videos with only a small number of samples, overcoming the limitation of limited samples in existing data-hungry learning algorithms. The proposed defensive mechanisms will ensure the robustness of the deepfake detector and prevent it from misclassifying camouflaged or obscured forged visual content as genuine content. The project will address false content detection and mitigate existing unresolved adversarial attacks in machine learning. The proposed lifelong learning mechanism will enable the deepfake detector to leverage accumulated knowledge to achieve self-improvement over time.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.