The widespread use of multimedia editing tools has facilitated the creation of realistic video forgeries, jeopardizing the trust in video content. To address frame duplication forgery, a prevalent technique, this paper introduces a novel algorithm leveraging QR decomposition (orthogonal-triangular decomposition) and Minkowski distance. The algorithm extracts frame features using QR decomposition and compares them with a reference frame using Minkowski distance. Candidate duplicates are identified through random block matching. We evaluate the proposed method on standard datasets (TDTVD, LASIESTA, and IVY LAB) and a self-generated dataset. Our method achieves exceptional performance, attaining a perfect ${\mathrm{F}}_1$-score for video-level detection on both the TDTVD and our self-generated datasets. Notably, for frame-level detection, it achieves an average accuracy of 0.9943, precision of 0.9752, recall of 0.9858, and ${\mathrm{F}}_1$-score of 0.9803 across all datasets. Our analysis demonstrates the proposed method demonstrates promising performance in detecting multiply-duplicated frames and shows robustness against post-processing, potentially outperforming existing approaches.