One of the drawbacks of the current revolution on media is that tampering with images and videos is an increasingly easy task which brings a situation where digital media cannot be trusted. Digital video forensics study the effects of attacks and tampering techniques on videos and has arisen as a solution to the problem of lack of trustworthiness on digital media. Copy-Move tampering is one of the most common attacks, with variants for delete and duplicate objects in videos, and has been studied in several video forensics works with different approaches. Despite that, there is not yet a simple method to determine multiple variants of Copy-Move attacks. This work proposes a simple yet effective method do detect Copy-Move for both subregion and full-frame duplication.
BagiwaM.A., WahabA.W.A., IdrisM.Y.I., KhanS. and RaymondC.K.-K., Chroma key background detection for digital video using statistical correlation of blurring artifact, Digital Investigation19 (2016), 29–43.
2.
BestaginiP., MilaniS., TagliasacchiM. and TubaroS., Local tampering detection in video sequences, In Multimedia Signal Processing (MMSP), 2013 IEEE 15th International Workshop on, 2013, pp. 488–493. IEEE.
3.
BestaginiP., Visentini-ScarzanellaM., TagliasacchiM., DragottiP.L. and TubaroS., Video recapture detection based on ghosting artifact analysis, In 2013 IEEE International Conference on Image Processing, 2013, pp. 4457–4461. IEEE.
4.
BidokhtiA. and GhaemmaghamiS., Detection of regional copy/move forgery in mpeg videos using optical flow, In Artificial Intelligence and Signal Processing (AISP), 2015 International Symposium on, 2015, pp. 13–17. IEEE.
5.
ChenM., FridrichJ., GoljanM. and LukášJ., Source digital camcorder identification using sensor photo response non-uniformity, In Security, Steganography, and Watermarking of Multimedia Contents IX, volume 6505, International Society for Optics and Photonics, 2007, p. 65051G.
6.
ChettyG., Blind and passive digital video tamper detection based on multimodal fusion, In Proc of the 14th WSEAS International Conference on Communications, 2010, pp. 109–117.
7.
ConotterV., O’BrienJ.F. and FaridH., Exposing digital forgeries in ballistic motion, IEEE Transactions on Information Forensics and Security7(1) (2012), 283–296.
8.
DirikA.E., SencarH.T. and MemonN., Digital single lens reflex camera identification from traces of sensor dust, IEEE Transactions on Information Forensics and Security3(3) (2008), 539–552.
9.
HsuC.-C., HungT.-Y., LinC.-W. and HsuC.-T., Video forgery detection using correlation of noise residue, In Multimedia Signal Processing, 2008 IEEE 10th Workshop on, 2008, pp. 170–174. IEEE.
10.
LiF. and HuangT., Video copy-move forgery detection and localization based on structural similarity, In Proceedings of the 3rd International Conference on Multimedia Technology (ICMT 2013), Springer, 2014, pp. 63–76.
11.
LinC.-S. and Jyh-JongT., A passive approach for effective detection and localization of region-level video forgery with spatio-temporal coherence analysis, Digital Investigation11(2) (2014), 120–140.
12.
MilaniS., FontaniM., BestaginiP., BarniM., PivaA., TagliasacchiM. and TubaroS., An overview on video forensics, APSIPA Transactions on Signal and Information Processing, 1, 2012.
13.
PandeyR.C., SinghS.K. and ShuklaK.K., Passive copy-move forgery detection in videos, In Computer and Communication Technology (ICCCT), 2014 International Conference on, 2014, pp. 301–306. IEEE.
14.
RajJ. and NairM.S., Forgery detection in ballistic motion videos using motion estimation and modelling, In Mining Intelligence and Knowledge Exploration, Springer, 2013, pp. 328–339.
SharmaS. and DhavaleS.V., A review of passive forensic techniques for detection of copy-move attacks on digital videos, In Advanced Computing and Communication Systems (ICACCS), 2016 3rd International Conference on, volume 1, 2016, pp. 1–6. IEEE.
17.
SinghR.D. and AggarwalN., Detection of upscale-crop and splicing for digital video authentication, Digital Investigation21 (2017), 31–52.
18.
SitaraK. and MehtreB.M., Digital video tampering detection: An overview of passive techniques, Digital Investigation18 (2016), 8–22.
19.
SuY., HanY. and ZhangC., Detection of blue screen based on edge features, In Information Technology and Artificial Intelligence Conference (ITAIC), 2011 6th IEEE Joint International, IEEE Computer Society, 2011, pp. 469–472.
20.
SubramanyamA.V. and EmmanuelS., Video forgery detection using HOG features and compression properties, In MMSP, 2012, pp. 89–94. IEEE.
21.
Visentini-ScarzanellaM. and DragottiP.L., Modelling radial distortion chains for video recapture detection, In Multimedia Signal Processing (MMSP), 2013 IEEE 15th International Workshop on, 2013, pp. 412–417. IEEE.
22.
WahabA.W.A., BagiwaM.A., IdrisM.Y.I., KhanS., RazakZ. and AriffinM.R.K., Passive video forgery detection techniques: A survey, In Information Assurance and Security (IAS), 2014 10th International Conference on, 2014, pp. 29–34. IEEE.
23.
WangQ., LiZ., ZhangZ. and MaQ., Video inter-frame forgery identification based on consistency of correlation coefficients of gray values, Journal of Computer and Communications2(04) (2014), 51.
24.
WangW., JiangX., WangS., WanM. and SunT., Identifying video forgery process using optical flow, In International Workshop on Digital Watermarking, Springer, 2013, pp. 244–257.
25.
WangW. and FaridH., Exposing digital forgeries in video by detecting double MPEG compression. In
VoloshynovskiyS.,
DittmannJ. and
FridrichJ.J., editors, Proceedings of the 8th workshop on Multimedia & Security, MM&Sec 2006, Geneva, Switzerland, 2006, pp. 37–47. ACM.
26.
WangW. and FaridH., Exposing digital forgeries in video by detecting duplication, In Proceedings of the 9th Workshop on Multimedia & Security, 2007, pp. 35–42. ACM.
27.
YangJ., HuangT. and LichaoS., Using similarity analysis to detect frame duplication forgery in videos, Multimedia Tools and Applications75(4) (2016), 1793–1811.
