Vision-Led Novelty Screening for Video Archives

This paper proposes a different approach to novelty verification. It presents a means of implementing checks on video submissions that is similar in logic to a computer vision ‘algorithm’. The pipeline sampling reference clips by a committee, along with learned visual descriptors, scene-temporal hashing, and near-duplicate retrieval of incoming stream videos w.r.t. repository entries, yields a quantification of quantitativeness. This leads to a statistically grounded accept/reject decision under uncertainty. The method highlights being robust against frequent manipulations (for example, re-encoding, cropping, style changes) but remaining sensitive to content reuse, also outlines integration with audit logs for provenance, re-playable reviews, and progressive confidence updates as models or thresholds change. The method scales by trading off computation for statistical power via controllable sampling. The method targets high recall against plagiarised/lite-edited submissions while not penalising authentic submissions that are visually very dissimilar.