Data-parallel full reference algorithm for dropped frame identification in uncompressed video using genetic algorithm

In recent years due to easy availability of video editing tools, video sequences which are to be presented as evidence during court trials, can be tampered and therefore misguide the court proceeding. These video sequences are required to be authenticated by forensic experts before considering them as evidence during court trials. Frame drop is one of the most often temporal tampering and it is required to authenticate a video sequence against frame drop. Current work is an extension of author´s pre-published work where they presented a full reference algorithm for dropped frame identification in uncompressed video using genetic algorithm which efficiently identifies dropped frame indices with accuracy ranging from 78 to 100 percentage under different test scenarios. To resolve the issue of scalability, this paper extends the algorithm by identifying set of independent tasks in pre-published FR algorithm and presented a data-parallel algorithm. We simulated the scheme over five video sequences (of duration 10 to 540 seconds) by dropping 0.5 percentage and 1 percentage video frames and analyzed the required processing time with 1, 2, 4, and 8 processors. Simulated results suggested that the presented algorithm is scalable and efficiently identifies dropped frame indices with average speedup of 1.77, 2.81, and 3.35 for 2, 4, and 8 processors respectively.