Issue

Vo. 6, No. 3, August 2021

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The Effect of Error Level Analysis on The Image Forgery Detection Using Deep Learning

https://doi.org/10.22219/kinetik.v6i3.1272
Wina Permana Sari
Bina Nusantara University, Jakarta
Hisyam Fahmi
Universitas Islam Negeri Malik Maulana Ibrahim Malang

Corresponding Author(s) : Hisyam Fahmi

hisyam.fahmi@uin-malang.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vo. 6, No. 3, August 2021

Abstract

Digital image modification or image forgery is easy to do today. The authenticity verification of an image become important to protect the image integrity so that the image is not being misused. Error Level Analysis (ELA) can be used to detect the modification in image by lowering the quality of image and comparing the error level. The use of deep learning approach is a state-of-the-art in solving cases of image data classification. This study wants to know the effect of adding ELA extraction process in the image forgery detection using deep learning approach. The Convolutional Neural Network (CNN), which is a deep learning method, is used as a method to do the image forgery detection. The impacts of applying different ELA compression levels, such as 10, 50, and 90 percent, were also compared in this study. According to the results, adopting the ELA feature increases validation accuracy by about 2.7% and give the better test accuracy. However, the use of ELA will slow down the processing time by about 5.6%.

Keywords

convolutional neural network copy-move image forgery digital image forensics image integrity
Sari, W. P., & Fahmi, H. (2021). The Effect of Error Level Analysis on The Image Forgery Detection Using Deep Learning. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 6(3). https://doi.org/10.22219/kinetik.v6i3.1272
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. R. Ahmed and R. V Dharaskar, “Study of Mobile Botnets: An Analysis from the Perspective of Efficient Generalized Forensics Framework for Mobile Devices General Terms,” in National Conference on Innovative Paradigms in Engineering & Technology (NCIPET), 2012, pp. 5–8.
  2. M. Yu, J. Zhang, S. Li, J. Lei, F. Wang, and H. Zhou, “Deep forgery discriminator via image degradation analysis,” IET Image Process., May 2021. https://doi.org/10.1049/ipr2.12234
  3. D. Chauhan, D. Kasat, S. Jain, and V. Thakare, “Survey on Keypoint Based Copy-move Forgery Detection Methods on Image,” in Procedia Computer Science, 2016, vol. 85. https://doi.org/10.1016/j.procs.2016.05.213
  4. A. D. Warbhe and R. V Dharaskar, “An Active Approach based on Independent Component Analysis for Digital Image Forensics.”.
  5. H. Farid, “Image forgery detection,” IEEE Signal Processing Magazine, vol. 26, no. 2. Institute of Electrical and Electronics Engineers Inc., pp. 16–25, 2009.https://doi.org/10.1109/MSP.2008.931079
  6. V. Conotter, G. Boato, and H. Farid, “Active and Passive Multimedia Forensics,” 2011.
  7. T. Kumar and G. Khurana, “Towards recent developments in the field of digital image forgery detection,” Int. J. Comput. Appl. Technol., vol. 58, no. 1, p. 1, 2018. https://doi.org/10.1504/IJCAT.2018.094064
  8. W. S. Sari and C. A. Sari, “A High Result in Wavelet Watermarking Using Singular Value Decomposition,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 4, no. 3, pp. 269–276, Jul. 2019. https://doi.org/10.22219/kinetik.v4i3.729
  9. M. Mishra and M. C. Adhikary, “Digital Image Tamper Detection Techniques - A Comprehensive Study,” Int. J. Comput. Sci. Bus. Informatics, vol. 2, no. 1, 2013.
  10. S. Sadeghi, S. Dadkhah, H. A. Jalab, G. Mazzola, and D. Uliyan, “State of the art in passive digital image forgery detection: copy-move image forgery,” Pattern Anal. Appl., vol. 21, no. 2, pp. 291–306, May 2018. https://doi.org/10.1007/s10044-017-0678-8
  11. B. Soni and D. Biswas, “Image Forensic using Block-based Copy-move Forgery Detection,” 2018. https://doi.org/10.1109/SPIN.2018.8474287
  12. S. Dehnie, T. Sencar, and N. Memon, “Digital image forensics for identifying computer generated and digital camera images,” in Proceedings - International Conference on Image Processing, ICIP, 2006, pp. 2313–2316. https://doi.org/10.1109/ICIP.2006.312849
  13. A. D. Warbhe, R. V. Dharaskar, and V. M. Thakare, “Computationally Efficient Digital Image Forensic Method for Image Authentication,” in Procedia Computer Science, 2016, pp. 464–470. https://doi.org/10.1016/j.procs.2016.02.089
  14. D. Y. Liliana and T. Basaruddin, “Deteksi Pemalsuan Citra Berbasis Dekomposisi Nilai Singulir,” MAKARA Sci. Ser., vol. 13, no. 2, pp. 180–184, 2010. https://doi.org/10.7454/mss.v13i2.422
  15. K.-T. Huynh, T.-N. Ly, and P.-T. Nguyen, “Improving the Accuracy in Copy-Move Image Detection: A Model of Sharpness and Blurriness,” SN Comput. Sci., vol. 2, no. 4, p. 278, Jul. 2021. https://doi.org/10.1007/s42979-021-00682-w
  16. N. A. Shelke and S. S. Kasana, “Multiple forgery detection and localization technique for digital video using PCT and NBAP,” Multimed. Tools Appl., pp. 1–29, May 2021. https://doi.org/10.1007/s11042-021-10989-8
  17. Y. Rao and J. Ni, “A deep learning approach to detection of splicing and copy-move forgeries in images,” Jan. 2017. https://doi.org/10.1109/WIFS.2016.7823911
  18. H. Phan-Xuan, T. Le-Tien, T. Nguyen-Chinh, T. Do-Tieu, Q. Nguyen-Van, and T. Nguyen-Thanh, “Preserving Spatial Information to Enhance Performance of Image Forgery Classification,” in International Conference on Advanced Technologies for Communications, Oct. 2019, vol. 2019-October, pp. 50–55. https://doi.org/10.1109/ATC.2019.8924504
  19. C. Chen, S. McCloskey, and J. Yu, “Image splicing detection via camera response function analysis,” in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017, vol. 2017-January. https://doi.org/10.1109/CVPR.2017.203
  20. B. Bayar and M. C. Stamm, “Constrained Convolutional Neural Networks: A New Approach Towards General Purpose Image Manipulation Detection,” IEEE Trans. Inf. Forensics Secur., vol. 13, no. 11, pp. 2691–2706, Nov. 2018. https://doi.org/10.1109/TIFS.2018.2825953
  21. H. Chen, C. Chang, Z. Shi, and Y. Lyu, “Hybrid features and semantic reinforcement network for image forgery detection,” Multimed. Syst., pp. 1–12, May 2021. https://doi.org/10.1007/s00530-021-00801-w
  22. Y. Zhang, J. Goh, L. L. Win, and V. Thing, “Image region forgery detection: A deep learning approach,” in Cryptology and Information Security Series, 2016, vol. 14, pp. 1–11. https://doi.org/10.3233/978-1-61499-617-0-1
  23. T. S. Gunawan, S. A. M. Hanafiah, M. Kartiwi, N. Ismail, N. F. Za’bah, and A. N. Nordin, “Development of photo forensics algorithm by detecting photoshop manipulation using error level analysis,” Indones. J. Electr. Eng. Comput. Sci., vol. 7, no. 1, pp. 131–137, Jul. 2017. http://doi.org/10.11591/ijeecs.v7.i1.pp131-137
  24. J. Dong, W. Wang, and T. Tan, “CASIA image tampering detection evaluation database,” in 2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings, 2013, pp. 422–426. https://doi.org/10.1109/ChinaSIP.2013.6625374
  25. N. B. A. Warif, A. W. A. Wahab, M. Y. I. Idris, R. Salleh, and F. Othman, “SIFT-Symmetry: A robust detection method for copy-move forgery with reflection attack,” J. Vis. Commun. Image Represent., vol. 46, pp. 219–232, Jul. 2017. https://doi.org/10.1016/j.jvcir.2017.04.004
  26. N. Krawetz, “A Picture’s Worth... Digital Image Analysis and Forensics,” 2007. Accessed: Sep. 28, 2020.
  27. H. Fahmi and W. P. Sari, “Effectiveness of Deep Learning Architecture for Pixel-Based Image Forgery Detection,” Apr. 2021, pp. 302–307. https://dx.doi.org/10.2991/assehr.k.210421.044
Read More

Author Biography

Wina Permana Sari, Bina Nusantara University, Jakarta

Computer Science Department, School of Computer Science

Download this PDF file
PDF
Statistic
Read Counter : 462 Download : 350

Downloads

Download data is not yet available.