Abstract
Video processing software is often used to remove specific moving foreground from a video. Existing forgery algorithms for detecting this type of tampering generally suffer from inefficiency and are not effective for the forged videos under complex background. To address these problems, we propose a novel forgery detection algorithm for detecting video foreground removal. The algorithm first calculates the energy factor (EF) of each frame to identify forged frames. An adaptive parameter-based visual background extractor (AVIBE) algorithm is then designed to detect suspected regions from the forged frames determined in the first stage. After eliminating false detection by calculating the difference of EF between suspected regions in the forged frames and the corresponding regions in the authentic frames, the algorithm finally locates the tampering traces. The experimental results show that our proposed algorithm has higher computational efficiency and accuracy as well as better robustness than those of previous algorithms.
Highlights
With the rapid development of multimedia technology and user-friendly editing software (e.g., Photoshop, Premiere by Adobe, and Mokey by Imagineer Systems), manipulating videos and changing their content is becoming a trivial task
In passive forensics, the veracity and integrity of a video are authenticated, typically without any validation information, which is more practical in real applications than active forensics [3], [8], [9]
The main contributions of this paper address the following elements: 1. Energy factor (EF) is constructed to identify forged frames, avoiding the deficiency of detecting videos frame by frame, which improves the detection efficiency greatly
Summary
With the rapid development of multimedia technology and user-friendly editing software (e.g., Photoshop, Premiere by Adobe, and Mokey by Imagineer Systems), manipulating videos and changing their content is becoming a trivial task. In [20], an algorithm based on compressive sensing for detecting moving foreground removed from static background is proposed. A fast passive forensic method to expose dynamic object removal and frames duplication using sensor noise features is proposed in [22]. What’s more, they are unable to detect videos under complex background (e.g., slightly shaking screens, swaying trees, water ripples, noise and brightness change) To address these problems, we propose a novel forgery detection algorithm for video foreground removal. Our algorithm is effective for detecting videos under different bit rate compression It realizes the detection of videos under complex background (e.g., slightly shaking screens, swaying trees, water ripples, noise and brightness change), which has better robustness and is more practical in applications.
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