This research project explores the use of generative AI tools for restoring and enhancing degraded video and audio content. The work focuses on two key use cases: (1) improving the quality of dash cam footage by addressing common real-world issues such as glare, shadows, and obstructions like dirty windshields; and (2) restoring old or damaged films and television recordings, including enhancing resolution, removing visual artifacts, and cleaning up audio. The project begins with a survey of current state-of-the-art models and tools, followed by hands-on experimentation and the development of novel approaches tailored to these restoration scenarios. The ultimate goal is to create accessible, AI-powered solutions that preserve visual media and improve its clarity and impact for both archival and contemporary use.

This research project investigates the ethical challenges posed by AI-generated and AI-modified video content, focusing on issues of copyright infringement, non-consensual content generation, and the tension between content moderation and freedom of expression. With a special focus on recent ACM CHI papers from the past five years, the study analyzes how human-computer interaction researchers have addressed these concerns through empirical studies, design frameworks, and policy recommendations. Topics explored include the unauthorized use of copyrighted material in AI-generated media, the psychological and societal harms of deepfake pornography, and how communities and platforms navigate the moderation of politically, religiously, or culturally sensitive AI content.