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Source Camera Identification for Online Social Network Images Using Texture Feature

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Book cover Recent Advances on Soft Computing and Data Mining (SCDM 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 978))

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Abstract

Nowadays, digital images are easily shared through social media and it common amongst internet users. It was a convenient way to share a moment and communicate with people all over the worlds through social media on the internet. However, this has caused the increasing number of crimes involving digital images in social media. It is well known that each digital image that passes through online social networks (OSNs) is explicitly modified by Web 2.0 tools. Thus, it is challenging for authorities to probe further, including identifying the source of the digital images. Considering this limitation, an alternative method to identify source camera based on the texture feature for OSNs images is proposed. This technique uses texture feature characteristics, namely, Gray Level Co-Occurrence Matrix (GLCM) and Gray Level Run-Length Matrix (GLRLM). Original and OSNs images were tested to determine whether the proposed method is robust for both image types and gives higher accuracy than previous methods. Four types of camera models were used in this research. The results prove that the method tested in this study is accurate with an average accuracy of 97.00% and 99.59% for original and OSNs images, respectively, and is capable to read up to 600 images.

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References

  1. Yang Z, Wilson C, Wang X, Gao T, Zhao BY, Dai Y (2014) Uncovering social network sybils in the wild. ACM Trans Knowl Discov Data (TKDD) 8(1):2

    Article  Google Scholar 

  2. Jin L, Chen Y, Wang T, Hui P, Vasilakos AV (2013) Understanding user behavior in online social networks: a survey. IEEE Commun Mag 51(9):144–150

    Article  Google Scholar 

  3. Abdalla A, Yayilgan SY (2014) A review of using online social networks for investigative activities. In: International conference on social computing and social media. Springer, pp 3–12

    Google Scholar 

  4. Redi JA, Taktak W, Dugelay J-L (2011) Digital image forensics: a booklet for beginners. Multimed Tools Appl 51(1):133–162

    Article  Google Scholar 

  5. Garfinkel S (2010) Digital forensics research: the next 10 years. Digit Investig 7:S64–S73

    Article  Google Scholar 

  6. Piva A (2013) An overview on image forensics. ISRN Signal Process

    Google Scholar 

  7. Ellison NB (2007) Social network sites: definition, history, and scholarship. J Comput Mediat Commun 13(1):210–230

    Article  MathSciNet  Google Scholar 

  8. Blog V (2012) World map of social networks

    Google Scholar 

  9. Zainudin NM, Merabti M, Llewellyn-Jones D (2010) A digital forensic investigation model for online social networking. In: Proceedings of the 11th annual conference on the convergence of telecommunications, networking & broadcasting, Liverpool, pp. 21–22

    Google Scholar 

  10. Huber M, Mulazzani M, Leithner M, Schrittwieser S, Wondracek G, Weippl E (2011) Social snapshots: digital forensics for online social networks. In: Proceedings of the 27th annual computer security applications conference. ACM, pp 113–122

    Google Scholar 

  11. Xu B, Wang X, Zhou X, Xi J, Wang S Source camera identification from image texture features. Neurocomputing 207:131–140

    Article  Google Scholar 

  12. Caviglione L, Coccoli M, Merlo A (2014) A taxonomy-based model of security and privacy in online social networks. Int J Comput Sci Eng 9(4):325–338

    Google Scholar 

  13. Rizi FS, Khayyambashi MR (2013) Profile cloning in online social networks. Int J Comput Sci Inf Secur 11(8):82

    Google Scholar 

  14. Adikari S, Dutta K (2014) Identifying fake profiles in linkedin. In: Pasific Asia conference on information systems, p. 278

    Google Scholar 

  15. Xiao C, Freeman DM, Hwa T (2015) Detecting clusters of fake accounts in online social networks. In: Proceedings of the 8th ACM workshop on artificial intelligence and security. ACM, pp 91–101

    Google Scholar 

  16. Murphy JP, Fontecilla A (2013) Social media evidence in government investigations and criminal proceedings: a frontier of new legal issues. Rich J Law Technol 19:11–14

    Google Scholar 

  17. Castiglione A, Cattaneo G, Cembalo M, Petrillo UF (2013) Experimentations with source camera identification and online social networks. J Ambient Intell Humaniz Comput 4(2):265–274

    Article  Google Scholar 

  18. Lukas J, Fridrich J, Goljan M (2005) Determining digital image origin using sensor imperfections. In: Electronic imaging, international society for optics and photonics, pp 249–260

    Google Scholar 

  19. Lukas J, Fridrich J, Goljan M (2006) Digital camera identification from sensor pattern noise. IEEE Trans Inf Forensics Secur 1(2):205–214

    Article  Google Scholar 

  20. Luks J, Fridrich J, Goljan M (2005) Digital bullet scratches for images. In: IEEE international conference on image processing, 2005. ICIP 2005, vol 3, IEEE, pp III–65–8

    Google Scholar 

  21. Van Lanh T, Chong K-S, Emmanuel S, Kankanhalli MS (2007) A survey on digital camera image forensic methods, In: 2007 IEEE international conference on multimedia and expo. IEEE, pp 16–19

    Google Scholar 

  22. Gharibi F, Akhlaghian F, RavanJamjah J, ZahirAzami B (2010) Using the local information of image to identify the source camera. In: The 10th IEEE international symposium on signal processing and information technology. IEEE, pp 515–519

    Google Scholar 

  23. Xie Y-J, Bao Y, Tong S-F, Yang Y-H (2013) Source digital image identification based on cross-correlation. In: Proceedings of the 2nd international conference on computer science and electronics engineering. Atlantis Press

    Google Scholar 

  24. Bayram S, Sencar H, Memon N, Avcibas I (2005) Source camera identification based on cfa interpolation. In: IEEE international conference on image processing, 2005. ICIP 2005, vol 3. IEEE, pp III–69–72

    Google Scholar 

  25. Long Y, Huang Y (2006) Image based source camera identification using de- mosaicking. In: IEEE workshop on multimedia signal processing, pp 419–424

    Google Scholar 

  26. Ho JS, Au OC, Zhou J, Guo Y (2010) Inter-channel demosaicking traces for digital image forensics. In: 2010 IEEE international conference on multimedia and expo (ICME). IEEE, pp 1475–1480

    Google Scholar 

  27. Hu Y, Li C-T, Lin X, Liu B-B (2012) An improved algorithm for camera model identification using inter-channel demosaicking traces. In: 2012 eighth international conference on intelligent information hiding and multimedia signal processing (IIH-MSP). IEEE, pp 325–330

    Google Scholar 

  28. Kharrazi M, Sencar HT, Memon N (2004) Blind source camera identification. In: 2004 International conference on image processing, 2004. ICIP’04, vol 1, IEEE, pp 709–712

    Google Scholar 

  29. Wang B, Guo Y, Kong X, Meng F (2009) Source camera identification forensics based on wavelet features. In: Fifth international conference on intelligent information hiding and multimedia signal processing, 2009. IIH-MSP’09. IEEE, pp 702–705

    Google Scholar 

  30. Gao S, Hu R-M, Tian G (2012) Using multi-step transition matrices for camera model identification. Int J Hybrid Inf Technol 5(2):275–288

    Google Scholar 

  31. Kulkarni N, Mane V (2015) Source camera identification using glcm. In: Advance computing conference (IACC), 2015 IEEE international. IEEE, pp 1242–1246

    Google Scholar 

  32. Gauglitz S, Hllerer T, Turk M (2011) Evaluation of interest point detectors and feature descriptors for visual tracking. Int J Comput Vis 94(3):335

    Article  Google Scholar 

  33. Tuytelaars T, Van Gool L (2004) Matching widely separated views based on affine invariant regions. Int J Comput Vis 59(1):61–85

    Article  Google Scholar 

  34. Aanes H, Lindbjerg-Dahl A, Steenstrup-Pedersen K (2012) Interesting interest points-a comparative study of interest point performance on a unique data set. Int J Comput Vis 97:18–35

    Google Scholar 

  35. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell (6):679–698

    Article  Google Scholar 

  36. Roberts LG (1963) Machine perception of three-dimensional soups, Ph.D. thesis

    Google Scholar 

  37. Conners RW, Harlow CA (1980) A theoretical comparison of texture algorithms. IEEE Trans Pattern Anal Mach Intell (3):204–222

    Article  Google Scholar 

  38. Mohanty AK, Senapati MR, Beberta S, Lenka SK (2013) Texture-based features for classification of mammograms using decision tree. Neural Comput Appl 23(3–4):1011–1017

    Article  Google Scholar 

  39. Mohamed SS, Salama MM (2005) Computer-aided diagnosis for prostate cancer using support vector machine. In: Medical imaging, international society for optics and photonics, pp 898–906

    Google Scholar 

  40. Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern (6):610–621

    Article  Google Scholar 

  41. Tang X (1998) Texture information in run-length matrices. IEEE Trans Image Process 7(11):1602–1609

    Article  Google Scholar 

  42. Dasarathy BV, Holder EB (1991) Image characterizations based on joint gray level run length distributions. Pattern Recogn Lett 12(8):497–502

    Article  Google Scholar 

  43. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Wit-ten IH (2009) The weka data mining software: an update. ACM SIGKDD explor Newsl 11(1):10–18

    Article  Google Scholar 

  44. Gloe T, Bhme R (2010) The dresden image database for benchmarking digital image forensics. J Digit Forensic Pract 3(2–4):150–159

    Article  Google Scholar 

  45. Bayram S, Sencar HT, Memon N, Avcibas I (2006) Improvements on source camera-model identification based on CFA interpolation. Proc WG 11:24–27

    Google Scholar 

  46. Chu A, Sehgal CM, Greenleaf JF (1990) Use of gray value distribution of run lengths for texture analysis. Pattern Recogn Lett 11(6):415–419

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, University of Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia

    Nordiana Rahim & Cik Feresa Mohd Foozy

Authors
  1. Nordiana Rahim
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  2. Cik Feresa Mohd Foozy
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Corresponding author

Correspondence to Nordiana Rahim .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Rozaida Ghazali

  2. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Nazri Mohd Nawi

  3. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Mustafa Mat Deris

  4. School of Information Technology, Deakin University, Geelong Waurn Ponds Campus, VIC, Australia

    Jemal H. Abawajy

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Rahim, N., Foozy, C.F.M. (2020). Source Camera Identification for Online Social Network Images Using Texture Feature. In: Ghazali, R., Nawi, N., Deris, M., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2020. Advances in Intelligent Systems and Computing, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-030-36056-6_28

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