Abstract
In this digital era of technology and software development tools, low-cost digital cameras and powerful video editing software (such as Adobe Premiere, Microsoft Movie Maker, and Magix Vegas) have become available for any common user. Through these softwares, editing the contents of digital videos became very easy. Frame duplication is a common video forgery attack which can be done by copying and pasting a sequence of frames within the same video in order to hide or replicate some events from the video. Many algorithms have been proposed in the literature to detect such forgeries from the video sequences through analyzing the spatial and temporal correlations. However, most of them are suffering from low efficiency and accuracy rates and high computational complexity. In this paper, we are proposing an efficient and robust frame duplication detection algorithm to detect duplicated frames from the video sequence based on the improved Levenshtein distance. Extensive experiments were performed on some selected video sequences captured by stationary and moving cameras. In the experimental results, the proposed algorithm showed efficacy compared with the state-of-the-art techniques.
Highlights
In our daily life, digital videos are playing a vital role in many fields of applications such as surveillance systems, medical fields, and criminal investigations
Because of the availability of low-cost digital video cameras and powerful video editing tools, it is easy for common users to edit the video contents without leaving any visual traces of forgeries
Digital video forensics is an emerging research area which aims at validating the authenticity of such videos [1]. e classification of digital video forensics is shown in Figure 1, where it can be divided into 3 categories: identification of the source camera, discrimination of computer-generated videos, and video forgery detection [1]
Summary
Digital videos are playing a vital role in many fields of applications such as surveillance systems, medical fields, and criminal investigations. 2. Related Work e frame duplication attacks can be detected from the tampered videos by using the existing digital image forgery detection techniques [10] as the video is a sequence of sequential images in one temporal (time t) and two spatial (x, y) dimensions. Related Work e frame duplication attacks can be detected from the tampered videos by using the existing digital image forgery detection techniques [10] as the video is a sequence of sequential images in one temporal (time t) and two spatial (x, y) dimensions It may not seem a good idea due to the huge computational complexity obtained rather than the complex scenarios that the videos have such as static scenes [11]. There is inability to differentiate between the duplicated frame pairs and highly similar frame pairs (misdetected or false positive frame pairs) for the videos with long time static or still scenes
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