Dual adaptive deep convolutional neural network for video forgery detection in 3D lighting environment

Author:

Vinolin V.,Sucharitha M.

Publisher

Springer Science and Business Media LLC

Subject

Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software

Reference29 articles.

1. Tralic, D., Zupancic, I., Grgic, S., Grgic, M.: CoMoFoD—new database for copy-move forgery detection. In: Proceedings ELMAR, pp. 49–54 (2013)

2. Ghorbani, M., Firouzmand, M., Faraahi, A.: DWT-DCT (QCD) based copy-move image forgery detection. In: 18th International Conference on Systems, Signals and Image Processing, pp. 1–4 (2011)

3. Su, L., Huang, T., Yang, J.: A video forgery detection algorithm based on compressive sensing. Multimedia Tools Appl. 74(17), 6641–6656 (2015)

4. Delp, E., Memon, N., Wu, M.: Digital forensics. IEEE Signal Proc. Mag. 26(2), 14–15 (2009)

5. Saddique, M., Asghar, K., Bajwa, U.I., Hussain, M., Habib, Z.: Spatial video forgery detection and localization using texture analysis of consecutive frames. Adv. Electr. Comput. Eng. 19(3), 97–108 (2019)

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