Collaborative Research: SaTC: EDU: A Lab-based Curriculum for Watermarking AI-Generated Content: Theory, Algorithms, and Robustness Testing

Despite its societal benefits, generative AI also raises many societal and legal concerns. For instance, it may be abused to generate harmful content and boost disinformation campaigns. Watermark-based detection of AI-generated content is a key technology to address these societal and legal concerns. Several companies--such as Google, OpenAI, Stability AI, and Microsoft--have deployed such techniques to watermark their AI-generated content. Despite the growing deployment and adoption of watermarking techniques, there remains an education gap among various stakeholders--such as students, government policymakers, the current workforce, and the imminent generation of workers--regarding the theoretical foundations, technical implementations, and practical applications of these watermark techniques. The objective of this project is to bridge this education gap in watermarking AI-generated content. This project will develop a systematic lab-based curriculum that offers a diverse range of laboratory exercises for hands-on experience and practical skills acquisition of watermarking AI-generated content. The project will also develop and release an Open-Watermark-Platform with integrated labs to enable hands-on learning on watermarking. The lab-based curriculum and other education materials developed in this project will train the next generation of workforce to meet the urgent needs of watermarking AI-generated content and enable continual learning for the current workforce and policymakers. This project is supported by the Secure and Trustworthy Cyberspace (SaTC) program, which funds proposals that address cybersecurity and privacy, and in this case, cybersecurity education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy. 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.