Inter-frame forgery detection based on differential energy of residue

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.