An Overview of Digital Video Tampering Detection Using Passive Methods and D-Hash Algorithm

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Video tampering and integrity detection can be defined as methods of alteration of the contents of the video which will enable it to hide objects, an occasion or adjust the importance passed on by the collection of images in the video. Modification of video contents is growing rapidly due to the expansion of the video procurement gadgets and great video altering programming devices. Subsequently verification of video files is transforming into something very vital. Video integrity verification aims to search out the hints of altering and subsequently asses the realness and uprightness of the video. These strategies might be ordered into active and passive techniques. Therefore our area of concern in this paper is to present our views on different passive video tampering detection strategies and integrity check. Passive video tampering identification strategies are grouped into consequent three classifications depending on the type of counterfeiting as: Detection of double or multiple compressed videos, Region altering recognition and Video inter-frame forgery detection. So as to detect the tampering of the video, it is split into frames and hash is generated for a group of frames referred to as Group of Pictures. This hash value is verified by the receiver to detect tampering.  


  • Keywords

    Video tampering detection, Region altering, Video forensics, anti-forensics, group of pictures

  • References

      [1] Piva, "An overview on image forensics," ISRN Signal Processing, vol. 2013

      [2] Subramanyam and S. Emmanuel, "Video forgery detection using HOG features and compression properties," in 2012

      [3] Ardizzone, E., Mazzola, G., 2015. Image Analysis and Processing | ICIAP 2015: 18th International Conference, Genoa, Italy, September 7-11, 2015, Proceedings, Part II. Springer International Publishing, Cham, Ch. A Tool to Support the Creation of Datasets of Tampered Videos, pp. 665{675.

      [4] Bidokhti, A., Ghaemmaghami, S., March 2015. Detection of regional copy/move forgery in MPEG videos using optical ow. In: Articial Intelligence and Signal Processing (AISP), 2015 International Symposium on. pp. 13 17.

      [5] Chao, J., Jiang, X., Sun, T., 2013. Digital Forensics and Watermaking: 11th InternationalWorkshop, IWDW2012, Shanghai, China, October 31 { November 3, 2012, Revised Selected Papers. Springer Berlin Heidelberg, Berlin, Heidelberg, Ch. A Novel Video Inter-frame Forgery Model Detection Scheme

      [6] Chen, W., Shi, Y. Q., 2009. Digital watermarking. Springer-Verlag, Berlin, Heidelberg, Ch. Detection of Double MPEG Compression Based on First Digit Statistics

      [7] Chetty, G., Biswas, M., Singh, R., 2010. Digital video tamper detection based on multimodal fusion of residue features. In: Network and System Security (NSS), 2010 4th International Conference on. IEEE

      [8] Cozzolino, D., Poggi, G., Verdoliva, L., Oct 2014. Copy-move forgery detection based on patchmatch. In: 2014 IEEE International Conference on Image Processing (ICIP)

      [9] D. Vazquez-Padin, M. Fontani, T. Bianchi, P. Comesana, A. Piva, and M. Barni, "Detection of video double encoding with GOP size estimation," in 2012

      [10] Dong, Q., Yang, G., Zhu, N., 2012. A MCEA based passive forensics scheme for detecting frame-based video tampering. Digital Investigation 9

      [11] Gironi A, Fontani M, Bianchi T, Piva A, Barni M,“AVideo Forensic Technique for Detecting Frame Deletion and Insertion”,In2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2014 May 4.

      [12] Hyun DK, Ryu SJ, Lee HY, Lee HK. 2013 Sep; Detection of upscalecrop and partial manipulation in surveillance video based on sensor pattern noise. Sensors. 13(9):12605–31.

      [13] Jiang, X., Wang, W., Sun, T., Shi, Y. Q., Wang, S., 2013. Detection of double compression in MPEG-4 videos based on markov statistics. Signal Processing Letters, IEEE 20 (5)

      [14] Jin H,“Research of Blind Forensics Algorithm on Digital Image

      [15] Tampering”,Indonesian Journal of Electrical Engineering and Computer Science,2014 July

      [16] Kobayashi M, Okabe T, Sato Y. 2009 Jan Detecting video forgeries based on noise characteristics. Springer Berlin Heidelberg;. p. 306–17.

      [17] Lin CS, Tsay JJ. July 2014 A passive approach for effective detection and localization of region-level video forgery with spatiotemporal coherence analysis. Digital Investigation.

      [18] Liu H, Li S, Bian S. 2014 May Detecting frame deletion in H 264 video.Springer International Publishing;. p. 262–70.

      [19] P. He, X. Jiang, T. Sun, S. Wang, B. Li, Y. Dong, "Frame-wise detection of relocated I-frames in double compressed H.264 videos based on convolutional neural network", J. Vis. Commun. Image Represent., vol. 48, pp. 149-158, Oct. 2017.

      [20] Richardson, I. E., 2003. H. 264 and MPEG-4 video compression: video coding for next-generation multimedia. John Wiley & Sons.

      [21] S. Chen, S. Tan, B. Li, J. Huang, "Automatic detection of object-based forgery in advanced video", IEEE Trans. Circuits Syst. Video Technol., Nov. 2016

      [22] S. Milani, M. Fontani, P. Bestagini, M. Barni, A. Piva, M. Tagliasacchi, and S. Tubaro, "An overview on video forensics," APSIPA Transactions on Signal and Information Processing, vol. 1

      [23] Shanableh T, “Detection of Frame Deletion for Digital Video Forensics”, Digital Investigation2013 Dec 31;10(4):350-60

      [24] Su L, Huang T, Yang J. A video forgery detection algorithm based on compressive sensing. Multimedia Tools and Applications. 74(17):6641–56.

      [25] W.Wang, H. Farid, "Exposing digital forgeries in video by detecting double quantization", Proc. 11th ACM Workshop Multimedia Secur., pp. 39-48, 2009.

      [26] Xu Z, Feng C, Zhang W, Xu Y,“Automatic Location of Frame Deletion Point for Digital Video Forensics”,InProceedings of the 2nd ACM workshop on Information hiding and multimedia security 2014 Jun 11

      [27] Y. Su, J. Xu, "Detection of double-compression in MPEG-2 videos" May 2010

      [28] Zheng L, Sun T, Shi YQ. Inter-frame video forgery detection based on block-wise brightness variance descriptor. Springer International Publishing; 2014 Oct. p. 18–30.




Article ID: 28444
DOI: 10.14419/ijet.v7i4.6.28444

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.