Li L, Wang X, Zhang W, Yang G, Hu G (2013) Detecting removed object from video with stationary background. Lect Notes Comput Sci, Adv Image Video Technol 5414:306–317 Kobayashi M, Okabe T, Sato Y (2009) Detecting video forgeries based on noise characteristics. Hyun D-K, Lee M-J, Ryu S-J, Lee H-Y, Lee H-K (2013) Forgery detection for surveillance video. In: Proc.10th Workshop on IEEE Multimedia Signal Processing. Hsu C, Hung T, Lin C (2008) Video forgery detection using correlation of noise residue. In: Image Processing (ICIP), 16th IEEE International Conference on, 2009. Sig Process Mag, IEEE 26(2):14–15įeng JZ, Song L, Yang XK, Zhang W (2009) Sub clustering K-SVD: Size variable dictionary learning for sparse representations. 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.Įdward D, Nasir M, Min W (2009) Digital forensics. In the second stage, the candidate duplications are confirmed through random block matching. 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. Next, the Euclidean distance is calculated between features of each frame and the reference frame. In the first stage, the features of each frame are obtained via SVD (Singular Value Decomposition). This paper proposes an effective similarity-analysis-based method for frame duplication detection that is implemented in two stages. However, few algorithms have been suggested for detecting this tampering operation. Duplication of selected frames from a video to another location in the same video is one of the most common methods of video forgery.
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