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能吸引人的眼睛，还能愚弄现有的计算机程序，现在可以通过先进的人工智能技术生成，俗称为“深度假”技术。恶意当事人可以利用新技术将受害者的脸换成令人不舒服或虚构的场景，并损害此人的声誉。深伪技术可能被利用来制造虚假新闻，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，或者煽动公共暴力，增加国家之间的冲突。该项目的目标是设计一种智能深伪检测器，能够评估数字视觉内容的完整性，并自动实时检测伪造图像或视频，防止它们传播。拟议研究的成功将为数十亿社交网络用户提供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。该项目团队由两名在图像处理和网络安全方面具有互补专业知识的研究人员组成。该项目将极大地提高篡改视觉内容检测的技术水平。提出的系统的独特之处在于它具有自学习和自我进化的能力，能够捕获由目前未知的深度假冒算法随着时间的推移而产生的更改和欺骗性视觉内容。提出的自进化机制将使深度伪检测器能够快速适应只有少量样本的新型伪造图像或视频，克服了现有数据饥饿型学习算法中样本有限的限制。建议的防御机制将确保深度假冒检测器的健壮性，并防止其将伪装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，并缓解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深度假冒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。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.