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Inter‐frame forgery marks a central type of forgery in surveillance videos, and involves three aspects – frame duplication, insertion, and deletion – under temporal domain. However, this forgery type has received little attention from scholars. More efforts have been on detecting only a single aspect of inter‐frame forgery. Furthermore, studies have confirmed that previous methods did not achieve high accuracy for all forgeries types with low computational loads at the same time. In this study, the proposed method establishes a framework that can simultaneously detect all aspects of inter‐frame forgeries. During the decoding process, the authors extract residue data of each frame from a video stream. Then spatial and temporal energies are exploited to illustrate data flow, and abnormal points are determined to detect forged frames. Noise ratios of forged and original frames are estimated for differentiating insertion from duplication attacks. Experimental results indicate that the proposed method achieves higher accuracy and lower computational time for detecting inter‐frame forgery. [ABSTRACT FROM AUTHOR]

Copy–Move (CM) is a common type of digital image forgery where a part of the original image is copied and pasted at another position in the same image. Sometimes, to increase the invisibility of the copying process, some processing procedures on the copied region are performed before or after pasting. Although block matching (BM) is the common well-used procedure for CM tempering detection, blind CM detection with geometric or intensity transformations is still a challenge. In this paper, we propose an efficient methodology for enhancing BM based Copy–Move forgery detection. The main contribution of this work is the utilization of polar representation to get the representative features for each block. The main feature used in this paper is the frequency of each block based on Fourier transform. The experimental results show the efficiency of the proposed method for detecting CM regions, even when the copied region has undergone severe image manipulations such as rotation, scaling, Gaussian blurring, brightness modification, JPEG compression and noise addition. [ABSTRACT FROM AUTHOR]