Duplication of selected frames from a video to another location in the same video is one of the most common methods of video forgery. However, few algorithms have been suggested for detecting this tampering operation. This paper proposes an effective similarityanalysis-based method for frame duplication detection that is implemented in two stages. In the first stage, the features of each frame are obtained via SVD (Singular Value Decomposition). Next, the Euclidean distance is calculated between features of each frame and the reference frame. After dividing the video sequence into overlapping sub-sequences, the similarities between the sub-sequences are calculated, and then our algorithm identifies those video sequences with high similarity as candidate duplications. In the second stage, the candidate duplications are confirmed through random block matching. The experimental results show that our algorithm provides detection accuracy that is higher than the previous algorithms, and it has an outstanding performance in terms of time efficiency.

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Therefore, it is valuable to predict ENSO early and accurately to minimize these effects. However, predicting the strength of ENSO remains a challenge due to its complexity (Sun et al., 2016;Timmermann et al., 2018). Also, the increasing diversity of ENSO behavior since 2000 has led to a growing interest in the type of ENSO events (Geng et al., 2020). ENSO can be mainly divided into Eastern Pacific (EP) and Central Pacific (CP) types (Yeh et al., 2009), based on the distribution of the Sea Surface Temperature Anomaly (SSTA) during its maturation phase. However, some events that the SSTA is relatively high over the central and eastern Pacific Ocean cannot be classified as CP or EP types. Zhang et al. ( 2019) classified ENSO into EP, CP, and a mixture of the two (MIX) types of EI Niño (La Niña). To the best of our knowledge, the definition of ENSO type has not come to an agreement. Because the effects of different ENSO types vary greatly, for example, different EI Niño events have a different impact on US winter temperatures (Yu et al., 2012) and the East Asian climate (Yuan & Yang, 2012). Hence, the prediction of ENSO type is important for improving the quality of climate forecasts.

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