Video interframe forgery detection: Classification,technique & new dataset

Abstract

Multimedia communication as well as other related innovations are gaining tremendous growth in the modern technological era. Even though digital content has traditionally proved to be a piece of legitimate evidence. But the latest technologies have lessened this trust, as a variety of video editing tools have been developed to modify the original video. Therefore, in order to resolve this problem, a new technique has been proposed for the detection of duplicate video sequences. The present paper utilizes gray values to extract Hu moment features in the current frame. These features are further used for classification of video as authentic or forged. Afterwards there was also need to validate the proposed technique using training and test dataset. But the scarcity of training and test datasets, however, is indeed one of the key problems to validate the effectiveness of video tampering detection techniques. In this perspective, the Video Forensics Library for Frame Duplication (VLFD) dataset has been introduced for frame duplication detection purposes. The proposed dataset is made of 210 native videos, in Ultra-HD and Full-HD resolution, captured with different cameras. Every video is 6 to 15 seconds in length and runs at 30 frames per second. All the recordings have been acquired in three different scenarios (indoor, outdoor, nature) and in landscape mode(s). VLFD includes both authentic and manipulated video files. This dataset has been created as an initial repository for manipulated video and enhanced with new features and new techniques in future.

Keywords

Video forgery detection interframe forgery frame duplication Hu moments Dataset library

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References

O.I.

Al-Sanjary,

A.A.

Ahmed and

Sulong, Development of a video tampering dataset for forensic investigation, Forensic science international 266 (2016), 565–572. doi:10.1016/j.forsciint.2016.07.013.

O.I.

Al-Sanjary,

A.A.

Ahmed and

Sulong, Development of a Video Tampering Dataset VTD, Youtube, https://www.youtube.com/channel/UCZuuu-iyZvPptbIUHT9tMrA.

O.I.

Al-Sanjary and

Sulong, Detection of video forgery: A review of literature, Journal of Theoretical & Applied Information Technology 74(2) (2015).

Aparicio-Díaz,

Cumplido,

M.L.

Pérez Gort and

Feregrino-Uribe, Temporal copy-move forgery detection and localization using block correlation matrix, Journal of Intelligent & Fuzzy Systems 36(5) (2019), 5023–5035. doi:10.3233/JIFS-179048.

Ardizzone,

Bruno and

Mazzola, Copy-move forgery detection by matching triangles of keypoints, IEEE Transactions on Information Forensics and Security 10(10) (2015), 2084–2094. doi:10.1109/TIFS.2015.2445742.

Bakas,

Naskar and

Dixit, Detection and localization of inter-frame video forgeries based on inconsistency in correlation distribution between Haralick coded frames, Multimedia Tools and Applications 78(4) (2019), 4905–4935. doi:10.1007/s11042-018-6570-8.

Chen,

Tan,

Li and

Huang, Automatic detection of object-based forgery in advanced video, IEEE Transactions on Circuits and Systems for Video Technology 26(11) (2015), 2138–2151. doi:10.1109/TCSVT.2015.2473436.

Cozzolino,

Poggi and

Verdoliva, Copy-move forgery detection based on patchmatch, in: 2014 IEEE International Conference on Image Processing (ICIP), IEEE, 2014, pp. 5312–5316. doi:10.1109/ICIP.2014.7026075.

D’Amiano,

Cozzolino,

Poggi and

Verdoliva, Video forgery detection and localization based on 3D patchmatch, in: 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), IEEE, 2015, pp. 1–6.

10.

Deep Kaur and

Kanwal, An analysis of image forgery detection techniques, Statistics, Optimization & Information Computing 7(2) (2019), 486–500.

11.

Dong,

Yang and

Zhu, A MCEA based passive forensics scheme for detecting frame-based video tampering, Digital Investigation 9(2) (2012), 151–159. doi:10.1016/j.diin.2012.07.002.

12.

S.M.

Fadl,

Han and

Li, Inter-frame forgery detection based on differential energy of residue, IET Image Processing 13(3) (2018), 522–528. doi:10.1049/iet-ipr.2018.5068.

13.

N.K.

Gill,

Garg and

E.A.

Doegar, A review paper on digital image forgery detection techniques, in: 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, 2017, pp. 1–7. doi:10.1109/ICCCNT.2017.8203904.

14.

C.-C.

Hsu,

T.-Y.

Hung,

C.-W.

Lin and

C.-T.

Hsu, Video forgery detection using correlation of noise residue, in: 2008 IEEE 10th Workshop on Multimedia Signal Processing, IEEE, 2008, pp. 170–174.

15.

M.-K.

Hu, Visual pattern recognition by moment invariants, IRE transactions on information theory 8(2) (1962), 179–187. doi:10.1109/TIT.1962.1057692.

16.

Johnston and

Elyan, A review of digital video tampering: From simple editing to full synthesis, Digital Investigation (2019).

17.

Kanwal,

Girdhar,

Kaur and

J.S.

Bhullar, Digital image splicing detection technique using optimal threshold based local ternary pattern, Multimedia Tools and Applications (2020), 1–18.

18.

Li,

Wang,

Zhang,

Yang and

Hu, Detecting removed object from video with stationary background, in: International Workshop on Digital Watermarking, Springer, 2012, pp. 242–252.

19.

S.-Y.

Liao and

T.-Q.

Huang, Video copy-move forgery detection and localization based on Tamura texture features, in: 2013 6th International Congress on Image and Signal Processing (CISP), Vol. 2, IEEE, 2013, pp. 864–868. doi:10.1109/CISP.2013.6745286.

20.

C.-S.

Lin and

J.-J.

Tsay, A passive approach for effective detection and localization of region-level video forgery with spatio-temporal coherence analysis, Digital Investigation 11(2) (2014), 120–140. doi:10.1016/j.diin.2014.03.016.

21.

C.-Y.

Lin and

S.-F.

Chang, Generating robust digital signature for image/video authentication, in: Multimedia and Security Workshop at ACM Multimedia, Vol. 98, Citeseer, 1998, pp. 49–54.

22.

G.-S.

Lin and

J.-F.

Chang, Detection of frame duplication forgery in videos based on spatial and temporal analysis, International Journal of Pattern Recognition and Artificial Intelligence 26(07) (2012), 1250017. doi:10.1142/S0218001412500176.

23.

G.-S.

Lin,

J.-F.

Chang and

C.-H.

Chuang, Detecting frame duplication based on spatial and temporal analyses, in: 2011 6th International Conference on Computer Science & Education (ICCSE), IEEE, 2011, pp. 1396–1399.

24.

Liu,

Li and

Bian, Detecting frame deletion in H. 264 video, in: International Conference on Information Security Practice and Experience, Springer, 2014, pp. 262–270. doi:10.1007/978-3-319-06320-1_20.

25.

Liu and

Huang, Exposing video inter-frame forgery by Zernike opponent chromaticity moments and coarseness analysis, Multimedia Systems 23(2) (2017), 223–238. doi:10.1007/s00530-015-0478-1.

26.

Mahmood,

Nawaz,

Irtaza,

Ashraf,

Shah and

M.T.

Mahmood, Copy-move forgery detection technique for forensic analysis in digital images, Mathematical Problems in Engineering 2016 (2016).

27.

Mahmood,

Nawaz,

Shah,

Khan,

Ashraf and

H.A.

Habib, Copy-move forgery detection technique based on DWT and Hu Moments, International Journal of Computer Science and Information Security (IJCSIS) 14(5) (2016).

28.

Otiniano-Rodrıguez,

Cámara-Chávez and

Menotti, Hu and Zernike moments for sign language recognition, in: Proceedings of International Conference on Image Processing, Computer Vision, and Pattern Recognition, 2012, pp. 1–5.

29.

H.D.

Panchal and

H.B.

Shah, Video tampering dataset development in temporal domain for video forgery authentication, Multimedia Tools and Applications (2020), 1–25.

30.

V.M.

Potdar,

Han and

Chang, A survey of digital image watermarking techniques, in: INDIN’05. 2005 3rd IEEE International Conference on Industrial Informatics, IEEE, 2005, pp. 709–716. doi:10.1109/INDIN.2005.1560462.

31.

Qadir,

Yahaya and

A.T.S.

Ho, Surrey University Library for Forensic Analysis (SULFA) of video content, 2012, http://sulfa.cs.surrey.ac.uk/.

32.

Shanableh, Detection of frame deletion for digital video forensics, Digital Investigation 10(4) (2013), 350–360. doi:10.1016/j.diin.2013.10.004.

33.

Sharma and

Kanwal, Multimedia forensics: Analysis, classification and future directions, Wireless Communication and Mathematics (2019), 131.

34.

Sharma,

Kanwal and

R.S.

Batth, An ontology of digital video forensics: Classification, research gaps & datasets, in: 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), IEEE, 2019, pp. 485–491. doi:10.1109/ICCIKE47802.2019.9004331.

35.

Shullani,

Fontani,

Iuliani,

Al Shaya and

Piva, VISION: A video and image dataset for source identification, 2017, https://lesc.dinfo.unifi.it/en/datasets.

36.

Shullani,

Fontani,

Iuliani,

O.A.

Shaya and

Piva, Vision: A video and image dataset for source identification, EURASIP Journal on Information Security 2017(1) (2017), 15. doi:10.1186/s13635-017-0067-2.

37.

R.D.

Singh and

Aggarwal, Detection of upscale-crop and splicing for digital video authentication, Digital Investigation 21 (2017), 31–52. doi:10.1016/j.diin.2017.01.001.

38.

R.D.

Singh and

Aggarwal, Detection and localization of copy-paste forgeries in digital videos, Forensic science international 281 (2017), 75–91. doi:10.1016/j.forsciint.2017.10.028.

39.

R.D.

Singh and

Aggarwal, Video content authentication techniques: A comprehensive survey, Multimedia Systems 24(2) (2018), 211–240. doi:10.1007/s00530-017-0538-9.

40.

T.R.

Singh,

K.M.

Singh and

Roy, Video watermarking scheme based on visual cryptography and scene change detection, AEU-International Journal of Electronics and Communications 67(8) (2013), 645–651. doi:10.1016/j.aeue.2013.01.008.

41.

V.K.

Singh,

Pant and

R.C.

Tripathi, Detection of frame duplication type of forgery in digital video using sub-block based features, in: International Conference on Digital Forensics and Cyber Crime, Springer, 2015, pp. 29–38. doi:10.1007/978-3-319-25512-5_3.

42.

Sitara and

B.M.

Mehtre, Digital video tampering detection: An overview of passive techniques, Digital Investigation 18 (2016), 8–22. doi:10.1016/j.diin.2016.06.003.

43.

Sowmya,

Chennamma and

Rangarajan, Video authentication using spatio temporal relationship for tampering detection, Journal of Information Security and Applications 41 (2018), 159–169. doi:10.1016/j.jisa.2018.07.002.

44.

Su,

Huang and

Yang, A video forgery detection algorithm based on compressive sensing, Multimedia Tools and Applications 74(17) (2015), 6641–6656. doi:10.1007/s11042-014-1915-4.

45.

Su,

Nie and

Zhang, A frame tampering detection algorithm for MPEG videos, in: 2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference, Vol. 2, IEEE, 2011, pp. 461–464. doi:10.1109/ITAIC.2011.6030373.

46.

Su,

Zhang and

Liu, Exposing digital video forgery by detecting motion-compensated edge artifact, in: 2009 International Conference on Computational Intelligence and Software Engineering, IEEE, 2009, pp. 1–4.

47.

Tao,

Jia and

You, Review of passive-blind detection in digital video forgery based on sensing and imaging techniques, in: International Conference on Optoelectronics and Microelectronics Technology and Application, Vol. 10244, International Society for Optics and Photonics, 2017, p. 102441C.

48.

Wang,

Li,

Zhang and

Ma, Video inter-frame forgery identification based on consistency of correlation coefficients of gray values, Journal of Computer and Communications 2(04) (2014), 51. doi:10.4236/jcc.2014.24008.

49.

Wang and

Farid, Exposing digital forgeries in video by detecting duplication, in: Proceedings of the 9th Workshop on Multimedia & Security, ACM, 2007, pp. 35–42. doi:10.1145/1288869.1288876.

50.

Wang and

Farid, Exposing digital forgeries in interlaced and deinterlaced video, IEEE Transactions on Information Forensics and Security 2(3) (2007), 438–449. doi:10.1109/TIFS.2007.902661.

51.

Wang,

Jiang,

Wang,

Wan and

Sun, Identifying video forgery process using optical flow, in: International Workshop on Digital Watermarking, Springer, 2013, pp. 244–257.

52.

Wang,

Xue,

Zheng,

Liu and

Li, Image forensic signature for content authenticity analysis, Journal of Visual Communication and Image Representation 23(5) (2012), 782–797. doi:10.1016/j.jvcir.2012.03.005.

53.

Wary and

Neelima, A review on robust video copy detection, International Journal of Multimedia Information Retrieval 8(2) (2019), 61–78. doi:10.1007/s13735-018-0159-x.

54.

Wu,

Jiang,

Sun and

Wang, Exposing video inter-frame forgery based on velocity field consistency, in: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2014, pp. 2674–2678. doi:10.1109/ICASSP.2014.6854085.

55.

Yan,

Mei and

Chunqin, SAR image target recognition based on Hu invariant moments and SVM, in: 2009 Fifth International Conference on Information Assurance and Security, Vol. 1, IEEE, 2009, pp. 585–588. doi:10.1109/IAS.2009.289.

56.

Yang,

Huang and

Su, Using similarity analysis to detect frame duplication forgery in videos, Multimedia Tools and Applications 75(4) (2016), 1793–1811. doi:10.1007/s11042-014-2374-7.

57.

D.-N.

Zhao,

R.-K.

Wang and

Z.-M.

Lu, Inter-frame passive-blind forgery detection for video shot based on similarity analysis, Multimedia Tools and Applications 77(19) (2018), 25389–25408. doi:10.1007/s11042-018-5791-1.