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

现在，先进的人工智能技术（俗称“深度伪造”技术）可以生成伪造和欺骗性的图像和视频，这些图像和视频不仅对人眼具有吸引力，而且还能欺骗现有的计算机程序。恶意的人可以利用这项新技术将受害者的脸换成不舒服的或虚构的场景，从而损害受害者的声誉。深度造假技术可能被用来制造虚假新闻，影响竞选结果，在金融市场制造混乱，用虚假的灾难场景愚弄公众，或者煽动公众暴力，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，并实时自动检测伪造的图像或视频并防止其传播。拟议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，并确保数字取证视觉内容的真实性，从而造福我们的社会。该项目团队由两名在图像处理和网络安全方面具有互补专长的研究人员组成。该项目将显著提高伪造视觉内容检测的技术水平。该系统的独特之处在于它具有自我学习和自我进化的能力，可以捕获由目前未知的深度伪造算法随着时间的推移产生的改变和欺骗性的视觉内容。所提出的自进化机制将允许深度伪造检测器仅使用少量样本即可快速适应新型伪造图像或视频，克服现有数据饥渴学习算法中有限样本的限制。所提出的防御机制将确保深度伪造检测器的鲁棒性，并防止其将伪装或模糊的伪造视觉内容误认为是真实内容。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。所提出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完善。该奖项反映了美国国家科学基金会的法定使命，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.