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Near-duplicate video content has taken the large storage space in the age of big data. Without respecting the copyright ethic, social media users mirror, resize, and/or hide certain online video content and re-upload it as new data. This research aims to avoid the complex and high-dimensional matching and present an efficient approach for detecting near-duplicate videos, this detection is based on feature extraction using visual, motion, and high-level features. Fast and adaptive bidimensional empirical mode decomposition is used to preserve the relevant data to the furthest extent possible during the low/high-frequency transition and vice-versa. In addition, for a generic model, the invariant moments are added to the aforementioned features in order to reinforce them against different video transformations such as rotating and scaling. Furthermore, the video frames are divided into blocks with a fixed number of features, this set of features is represented by a signature, where its mean and standard deviation represents a single video map allowing easy similarity computation. The F1-score and accuracy are used to evaluate the results of this study; the relevant results are ranked by  Top    $$_{1}$$  1 for the best result, and the five top-ranked results are presented by  Top    $$_{5}$$  5 . Further, our result of  Top    $$_{1}$$  1 reached over 80% on F1-score, with a difference of ±4% from the  Top    $$_{5}$$  5 results, and it is over 90% on Accuracy using different datasets, such as UCF11, UCF50, and HDMB51. 

Video block and FABEMD features for an effective and fast method of reporting near-duplicate and mirroring videos