Skip to main content
SpringerLink
Log in

Exposing forgeries in soccer images using geometric clues

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In this study, new algorithms are proposed for exposing forgeries in soccer images. We propose a new and automatic algorithm to extract the soccer field, field side and the lines of field in order to generate an image of real lines for forensic analysis. By comparing the image of real lines and the lines in the input image, the forensic analyzer can easily detect line displacements of the soccer field. To expose forgery in the location of a player, we measure the height of the player using the geometric information in the soccer image and use the inconsistency of the measured height with the true height of the player as a clue for detecting the displacement of the player. In this study, two novel approaches are proposed to measure the height of a player. In the first approach, the intersections of white lines in the soccer field are employed for automatic calibration of the camera. We derive a closed-form solution to calculate different camera parameters. Then the calculated parameters of the camera are used to measure the height of a player using an interactive approach. In the second approach, the geometry of vanishing lines and the dimensions of soccer gate are used to measure a player height. Various experiments using real and synthetic soccer images show the efficiency of the proposed algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Amiri SH, Jamzad M (2014) Robust watermarking against print and scan attack through efficient modeling algorithm. Signal Process Image Commun 29(10):1181–1196

    Article  Google Scholar 

  2. Babaee-Kashany V, Pourreza HR (2010) Camera pose estimation in soccer scenes based on vanishing points. In: IEEE International Symposium on Haptic Audio-Visual Environments and Games (HAVE), IEEE, pp 1–6

  3. Birajdar GK, Mankar VH (2013) Digital image forgery detection using passive techniques: a survey. Digit Investig 10(3):226–245

    Article  Google Scholar 

  4. Cao H, Kot AC (2009) Accurate detection of demosaicing regularity for digital image forensics. IEEE Transactions on Information Forensics and Security 4(4):899–910

    Article  Google Scholar 

  5. Caprile B, Torre V (1990) Using vanishing points for camera calibration. Int J Comput Vis 4(2):127–139

    Article  Google Scholar 

  6. Chen M, Fridrich J, Lukáš J, Goljan M (2007) Imaging sensor noise as digital x-ray for revealing forgeries. In: Information hiding, Springer, pp 342–358

  7. Chen Z, Zhao Y, Ni R (2017) Detection of operation chain: JPEG-resampling-JPEG. Signal Process Image Commun 57:8–20

    Article  Google Scholar 

  8. Chierchia G, Poggi G, Sansone C, Verdoliva L (2014) A Bayesian-MRF approach for PRNU-based image forgery detection. IEEE Transactions on Information Forensics and Security 9(4):554–567

    Article  Google Scholar 

  9. Choi CH, Lee HY, Lee HK (2013) Estimation of color modification in digital images by CFA pattern change. Forensic Sci Int 226(1):94–105

    Article  Google Scholar 

  10. Criminisi A, Reid I, Zisserman A (2000) Single view metrology. Int J Comput Vis 40(2):123–148

    Article  Google Scholar 

  11. Desurmont X, Hayet J, Delaigle J, Piater J, Macq B (2006) Trictrac video dataset: Public hdtv synthetic soccer video sequences with ground truth. In: Workshop on Computer Vision Based Analysis in Sport Environments (CVBASE), pp 92–100

  12. Emam M, Han Q, Niu X (2016) PCET based copy-move forgery detection in images under geometric transforms. Multimedia Tools and Applications 75(18):11513–11527

    Article  Google Scholar 

  13. Emam M, Han Q, Zhang H (2017) Two-stage Keypoint detection scheme for region duplication forgery detection in digital images. J Forensic Sci. https://doi.org/10.1111/1556-4029.13456

  14. Farid H (2009) Image forgery detection--a survey. IEEE Signal Process Mag 26(2):16–25

    Article  Google Scholar 

  15. Farid H, Bravo MJ (2010) Image forensic analyses that elude the human visual system. In: IS&T/SPIE Electronic Imaging. vol 2. International Society for Optics and Photonics, pp 754106–754106-754110

  16. Farin D, Krabbe S, Effelsberg W (2004) Robust camera calibration for sport videos using court models. In: Electronic Imaging, International Society for Optics and Photonics, pp 80–91

  17. Farin D, Han J, de With PHN (2005) Fast camera calibration for the analysis of sport sequences. In: IEEE International Conference on Multimedia and Expo (ICME 2005), IEEE, p 4

  18. Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395

    Article  MathSciNet  Google Scholar 

  19. Fitzgibbon A, Pilu M, Fisher RB (1999) Direct least square fitting of ellipses. IEEE Trans Pattern Anal Mach Intell 21(5):476–480

    Article  Google Scholar 

  20. Ge H, Fang S (2015) Detecting image forgery using linear constraints based on shading and shadows. In: International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS), IEEE, pp 116–121

  21. Hashimoto S, Ozawa S (2006) A system for automatic judgment of offsides in soccer games. In: IEEE International Conference on Multimedia and Expo, IEEE, pp 1889–1892

  22. Hu WC, Chen WH, Huang DY, Yang CY (2016) Effective image forgery detection of tampered foreground or background image based on image watermarking and alpha mattes. Multimedia Tools and Applications 75(6):3495–3516

    Article  Google Scholar 

  23. Hwang MG, Kim SM, Har DH (2017) A method of identifying digital images with geometric distortion. Aust J Forensic Sci 49(1):93–105. https://doi.org/10.1080/00450618.2015.1128970

    Article  Google Scholar 

  24. Jabri I (2011) Camera calibration using court models for real-time augmenting soccer scenes. Multimedia Tools and Applications 51(3):997–1011

    Article  Google Scholar 

  25. Jin G, Wan X (2017) An improved method for SIFT-based copy-move forgery detection using non-maximum value suppression and optimized J-linkage. Signal Process Image Commun 57:113–125

    Article  Google Scholar 

  26. Johnson MK, Farid H (2006) Exposing digital forgeries through chromatic aberration. In: 8th workshop on Multimedia and Security, ACM, pp 48–55

  27. Johnson MK, Farid H (2007) Exposing digital forgeries in complex lighting environments. IEEE Transactions on Information Forensics and Security 2(3):450–461

    Article  Google Scholar 

  28. Kee E, O'Brien JF, Farid H (2013) Exposing photo manipulation with inconsistent shadows. ACM Transactions on Graphics (ToG) 32(3):28

    Article  Google Scholar 

  29. Khatoonabadi SH, Rahmati M (2009) Automatic soccer players tracking in goal scenes by camera motion elimination. Image Vis Comput 27(4):469–479

    Article  Google Scholar 

  30. Kim H, Hong KS (2001) Robust image mosaicing of soccer videos using self-calibration and line tracking. Pattern Analysis & Applications 4(1):9–19

    Article  MathSciNet  Google Scholar 

  31. Lagarias JC, Reeds JA, Wright MH, Wright PE (1998) Convergence properties of the Nelder--mead simplex method in low dimensions. SIAM J Optim 9(1):112–147

    Article  MathSciNet  Google Scholar 

  32. Li B, Shi YQ, Huang J (2008) Detecting doubly compressed JPEG images by using mode based first digit features. In: IEEE 10th Workshop on Multimedia Signal Processing, IEEE, pp 730–735

  33. Li X, Ma J, Wang W, Xiong Y, Zhang J (2013) A novel smart card and dynamic ID based remote user authentication scheme for multi-server environments. Math Comput Model 58(1):85–95. https://doi.org/10.1016/j.mcm.2012.06.033

    Article  Google Scholar 

  34. Liang Z, Yang G, Ding X, Li L (2015) An efficient forgery detection algorithm for object removal by exemplar-based image inpainting. J Vis Commun Image Represent 30:75–85

    Article  Google Scholar 

  35. Liao X, Qin Z, Ding L (2017) Data embedding in digital images using critical functions. Signal Process Image Commun 58:146–156. https://doi.org/10.1016/j.image.2017.07.006

    Article  Google Scholar 

  36. Liao X, Yin J, Guo S, Li X, Sangaiah AK (2017) Medical JPEG image steganography based on preserving inter-block dependencies. Computers & Electrical Engineering. https://doi.org/10.1016/j.compeleceng.2017.08.020

  37. Lin Z, He J, Tang X, Tang CK (2009) Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis. Pattern Recogn 42(11):2492–2501

    Article  Google Scholar 

  38. Liu Y, Liang D, Huang Q, Gao W (2006) Extracting 3D information from broadcast soccer video. Image Vis Comput 24(10):1146–1162

    Article  Google Scholar 

  39. Lukas J, Fridrich J, Goljan M (2006) Digital camera identification from sensor pattern noise. IEEE Transactions on Information Forensics and Security 1(2):205–214

    Article  Google Scholar 

  40. Malviya P, Naskar R (2014) Digital Forensic Technique for Double Compression based JPEG Image Forgery Detection. In: International Conference on Information Systems Security, Springer, pp 437–447

  41. Mayer O, Stamm M (2016) Improved forgery detection with lateral chromatic aberration. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2024–2028

  42. Meng X, Hu Z (2003) A new easy camera calibration technique based on circular points. Pattern Recogn 36(5):1155–1164

    Article  Google Scholar 

  43. Milani S, Tagliasacchi M, Tubaro S (2013) Antiforensics attacks to Benford's law for the detection of double compressed images. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) IEEE, pp 3053–3057

  44. Milani S, Tagliasacchi M, Tubaro S (2014) Discriminating multiple JPEG compressions using first digit features. Transactions on Signal and Information Processing (APSIPA ) 3:1–10

    Article  Google Scholar 

  45. O'Brien JF, Farid H (2012) Exposing photo manipulation with inconsistent reflections. ACM Trans Graph 31(1):4:1–4:11

    Article  Google Scholar 

  46. Peng F, Zhou DL (2014) Discriminating natural images and computer generated graphics based on the impact of CFA interpolation on the correlation of PRNU. Digit Investig 11(2):111–119. https://doi.org/10.1016/j.diin.2014.04.002

    Article  Google Scholar 

  47. Peng F, Nie YY, Long M (2011) A complete passive blind image copy-move forensics scheme based on compound statistics features. Forensic Sci Int 212(1):e21–e25. https://doi.org/10.1016/j.forsciint.2011.06.011

    Article  Google Scholar 

  48. Popescu AC, Farid H (2005) Exposing digital forgeries in color filter array interpolated images. IEEE Trans Signal Process 53(10):3948–3959

    Article  MathSciNet  Google Scholar 

  49. Qazi T, Hayat K, Khan SU, Madani SA, Khan IA, Kołodziej J, Li H, Lin W, Yow KC, Xu CZ (2013) Survey on blind image forgery detection. IET Image Process 7(7):660–670

    Article  Google Scholar 

  50. Saadat S, Moghaddam ME, Mohammadi M (2015) A new approach for copy-move detection based on improved weber local descriptor. J Forensic Sci 60(6):1451–1460

    Article  Google Scholar 

  51. Singh N, Bansal R (2015) Analysis of Benford's law in digital image forensics. In: International Conference on Signal Processing and Communication (ICSC), IEEE, pp 413–418

  52. Sirisantisamrid K, Matsuura T, Tirasesth K (2004) A simple technique to determine calibration parameters for coplanar camera calibration. In: IEEE Region 10 Conference (TENCON) 2004, IEEE, pp 677–680

  53. Szenberg F, Carvalho PCP, Gattass M (2001) Automatic camera calibration for image sequences of a football match. In: International Conference on Advances in Pattern Recognition, Springer, pp 303–312

  54. Taimori A, Razzazi F, Behrad A, Ahmadi A, Babaie-Zadeh M (2016) Quantization-unaware double JPEG compression detection. Journal of Mathematical Imaging and Vision 54(3):269–286

    Article  MathSciNet  Google Scholar 

  55. Taimori A, Razzazi F, Behrad A, Ahmadi A, Babaie-Zadeh M (2017) A novel forensic image analysis tool for discovering double JPEG compression clues. Multimedia Tools and Applications 76(6):7749–7783

    Article  Google Scholar 

  56. Thajeel SA, Sulong G (2014) A survey of copy-move forgery detection techniques. Journal of Theoretical & Applied Information Technology 70(1):25–35

    Google Scholar 

  57. Thing VL, Chen Y, Cheh C (2012) An improved double compression detection method for JPEG image forensics. In: IEEE International Symposium on Multimedia (ISM), IEEE, pp 290–297

  58. Tsai R (1987) A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE Journal on Robotics and Automation 3(4):323–344

    Article  Google Scholar 

  59. Warif NBA, Wahab AWA, Idris MYI, Ramli R, Salleh R, Shamshirband S, Choo K-KR (2016) Copy-move forgery detection: survey, challenges and future directions. J Netw Comput Appl 75:259–278

    Article  Google Scholar 

  60. Wu L, Wang Y (2010) Detecting image forgeries using geometric cues. In: Computer Vision for Multimedia Applications: Methods and Solutions, p 197

  61. Zhong J, Gan Y, Young J, Huang L, Lin P (2017) A new block-based method for copy move forgery detection under image geometric transforms. Multimedia Tools and Applications 76(13):14887–14903

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electrical Engineering Department, Shahed University, Tehran, Iran

    Morteza Nasiri & Alireza Behrad

Authors
  1. Morteza Nasiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alireza Behrad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alireza Behrad.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nasiri, M., Behrad, A. Exposing forgeries in soccer images using geometric clues. Multimed Tools Appl 77, 31363–31396 (2018). https://doi.org/10.1007/s11042-018-6225-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6225-9

Keywords

Search

Navigation