Perceptual Hash Based Content Matching

The proliferation of video content and AI generated imagery has introduced a number of new challenges in content identification and verification. The ability to trace content back to its source has become a critical problem as video content increases both naturally and synthetically through AI generation. This thesis provides the design, analysis, and experimental verification for a perceptual hash based framework aimed at addressing these challenges. Perceptual hashing is a method for encoding the visual content of images into compact and easily comparable binary strings. This process is used as the foundation for content matching in videos and source verification in AI generated content. The proposed content matching process is evaluated through two primary applications. The process is first applied to video content in which individual frames are matched across a dataset of car crash compilations. This experiment demonstrates the effectiveness of the perceptual hash in locating identical frames that may have minor visual transformations. The process is then applied to AI generated images, where its ability to identify the source image is tested. Through varying denoising parameters during the AI image generation process, the method’s sensitivity to subtle changes in image content can be assessed. The results of this thesis demonstrate the advantages and disadvantages of perceptual hashing in both applications