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In this paper, we present a passive-blind scheme for detection of frame duplication forgery in videos. The scheme is a coarse-to-fine approach that is implemented in four stages: candidate segment selection, spatial similarity measurement, frame duplication classification, and post-processing. To screen and select duplicated candidates in the temporal domain, the histogram difference of two adjacent frames in the RGB color space is adopted as a feature. Then, to evaluate the similarity of two images, we use a block-based algorithm to measure the spatial correlation between the candidate segment and the corresponding frame in the query template. Based on the results of spatial and temporal analysis, we construct a classifier to detect duplicated clips. In addition, to deal with the partial detection problem, we develop a post-processing technique that examines and merges two adjacent detected candidates into a com-plete duplicated video clip. Our experiment results demonstrate that the proposed scheme can not only achieve detection of frame duplication forgery but also accurately detect and localize duplicated clips in different kinds of videos. The results also show that the scheme outperforms an existing method in terms of precision, recall, accuracy, and computation time. [ABSTRACT FROM AUTHOR]