Skip to main content
SpringerLink
Log in

RETRACTED ARTICLE: Splicing image forgery identification based on artificial neural network approach and texture features

  • Published:
Cluster Computing Aims and scope Submit manuscript

This article was retracted on 20 December 2022

This article has been updated

Abstract

Splicing Image Forgery Identification controls and picture tampering with no proof being left behind has turned out to be exceptionally moderate and exceedingly utilized, because of presence of to a great degree intense altering apparatuses, for example, Adobe Photoshop. Along these lines, there has been a quick expansion of the digitally adjusted pictures on the Internet and prevailing press. The genuineness of a digital image experiences extreme dangers because of the ascent of capable digital image altering devices that effectively adjust the image substance without leaving any obvious hints of such changes. The splicing forgery should be possible by replicated a one/more area from source image and pasted into an objective picture to create a composite image called spliced image. In this manner, this sort of forgery is viewed as challenge issue difficult from tamper identification perspective. To influence the issue most exceedingly awful some to post preparing impacts, for example, blurring, JPEG compression, rotation and scaling perhaps presented in the spliced image. This study aims to perform quantification and data analysis following feature extraction using computational techniques to detect interesting textural and anatomical changes, these extracted features then can be used as a key to distinguish between different classes (Splicing Image and non-Splicing image). This paper presents a new framework to identify the spliced image by exploiting the image texture features, and to automatically identification of spliced images. To accurately identify the spliced image, the proposed solution uses different texture features to capture deferent texture related to the edge of the object and the colour features. We have combine these features to produce a good vector to describe the splice object. Two models have been proposed in this paper first combine the vectors of the three features and feed them to the ANN classifier. Second, use the majority voting of the result of three features to take the decision. This is followed by a ANN classifier; in this model we have trained the system with 30 training 20% validation 50% testing. We evaluated the effectiveness of the classification framework for identifying spliced images by compare the result with the manual label which is done by the people who have created the data sets. In this approach we have combine different features to capture different information and feed them to the neural network to identify the splicing image. The findings outcome from this study have shown an improved approach that automatically splicing image forgery identification. We have evaluated the splicing image forgery identification using the texture features. The identification accuracy in the technique used is about 98.06%., with 99.03% sensitivity and 96.07% specificity.

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

Similar content being viewed by others

Change history

References

  1. Sridevi, M., Mala, C., Sanyam, S.: Comparative study of image forgery and copy-move techniques. Adv. Comput. Sci. Eng. Appl. 715–723 (2012)

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

    Article  Google Scholar 

  3. Hashem, F.S., Sulong, G.: Passive aproaches for detecting image tampering: a review. J. Teknologi 73(2), 31–36 (2015)

    Google Scholar 

  4. Man, M., Zaki, F.A.M., Zakaria, M.Z., Rakhmadi, A., Othman, N.Z.S., Rahim, M.S.M.: FishDTecTools: fish detection solution using neural network approach. J. Commun. Comput. 8(2), 96–102 (2011)

    Google Scholar 

  5. Ghorbani, M., Firouzmand, M., Faraahi, A.: DWT-DCT (QCD) based copy-move image forgery detection. In: 2011 18th International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 1–4. IEEE (2011 June)

  6. Zhao, X., Li, J., Li, S., Wang, S.: Detecting digital image splicing in chroma spaces. In: International Workshop on Digital Watermarking, pp. 12–22. Springer, Berlin (2010 October)

  7. Alahmadi, A.A., Hussain, M., Aboalsamh, H., Muhammad, G., Bebis, G.: Splicing image forgery detection based on DCT and local binary pattern. In: Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE, pp. 253–256. IEEE (2013 December)

  8. Johnson, M.K., Farid, H.: Detecting photographic composites of people. In: International Workshop on Digital Watermarking, pp. 19–33. Springer, Berlin (2007 December)

  9. Johnson, M.K., Farid, H.: Exposing digital forgeries through chromatic aberration. In: Proceedings of the 8th Workshop on Multimedia and Security, pp. 48–55. ACM (2006 September)

  10. Mohammed, M.A., Ghani, M.K.A., Hamed, R.I., Abdullah, M.K., Ibrahim, D.A.: Automatic segmentation and automatic seed point selection of nasopharyngeal carcinoma from microscopy images using region growing based approach. J. Comput. Sci. 20, 61–69 (2017)

    Article  Google Scholar 

  11. Mohammed, M.A., Ghani, M.K.A., Hamed, R.I., Ibrahim, D.A., Abdullah, M.K.: Artificial neural networks for automatic segmentation and identification of nasopharyngeal carcinoma. J. Comput. Sci. 21, 263–274 (2017)

    Article  Google Scholar 

  12. Mohammed, M.A., Ghani, M.K.A., Hamed, R.I., Mostafa, S.A., Ibrahim, D.A., Jameel, H.K., Alallah, A.H.: Solving vehicle routing problem by using improved K-nearest neighbor algorithm for best solution. J. Comput. Sci. 21, 232–240 (2017)

    Article  Google Scholar 

  13. Mohammed, M.A., Ghani, M.K.A., Hamed, R.I., Mostafa, S.A., Ahmad, M.S., Ibrahim, D.A.: Solving vehicle routing problem by using improved genetic algorithm for optimal solution. J. Comput. Sci. 21, 255–262 (2017)

    Article  Google Scholar 

  14. Nikias, C.L.: Higher-order spectral analysis. In: Engineering in Medicine and Biology Society, 1993. Proceedings of the 15th Annual International Conference of the IEEE, pp. 319–319. IEEE (1993)

  15. Andrew, J.P., Kaisha, C.K.: Two dimensional discrete wavelet transforms. U.S. Patent 7,277,489 (2007)

  16. Banerji, S., Verma, A., Liu, C.: Novel color LBP descriptors for scene and image texture classification. In: 15th International Conference on Image Processing, Computer Vision, and Pattern Recognition, Las Vegas, Nevada, pp. 537–543 (2011 July)

  17. Bratkova, M., Boulos, S., Shirley, P.: oRGB: a practical opponent color space for computer graphics. IEEE Comput. Graph. Appl. 29(1) (2009)

  18. Heikkilä, M., Pietikäinen, M., Schmid, C.: Description of interest regions with center-symmetric local binary patterns. In: ICVGIP, vol. 6, pp. 58–69 (2006 December)

  19. Bai, Y., Guo, L., Jin, L., Huang, Q.: A novel feature extraction method using pyramid histogram of orientation gradients for smile recognition. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 3305–3308. IEEE (2009 November)

  20. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, 2005. IEEE Computer Society Conference on CVPR 2005, vol. 1, pp. 886–893. IEEE (2005 June)

  21. Wang, X., Han, T.X., Yan, S.: An HOG-LBP human detector with partial occlusion handling. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 32–39. IEEE (2009 September)

  22. Zhu, Q., Yeh, M.C., Cheng, K.T., Avidan, S.: Fast human detection using a cascade of histograms of oriented gradients. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1491–1498. IEEE (2006)

  23. Mohammed, M.A., Ghani, M.K.A., Hamed, R.I., Ibrahim, D.A.: Review on Nasopharyngeal Carcinoma: concepts, methods of analysis, segmentation, classification, prediction and impact: a review of the research literature. J. Comput. Sci. 21, 283–298 (2017)

    Article  Google Scholar 

  24. Mohammed, M.A., Ghani, M.K.A., Hamed, R.I., Ibrahim, D.A.: Analysis of an electronic methods for nasopharyngeal carcinoma: prevalence, diagnosis, challenges and technologies. J. Comput. Sci. 21, 241–254 (2017)

  25. Wang, S.C.: Artificial neural network. In: Interdisciplinary Computing in Java Programming, pp. 81–100. Springer, New York (2003)

  26. Mahdian, B., Saic, S.: A bibliography on blind methods for identifying image forgery. Sig. Process. Image Commun. 25(6), 389–399 (2010)

    Article  Google Scholar 

  27. Wu, T.K., Meng, Y.R., Huang, S.C.: Application of artificial neural network to the identification of students with learning disabilities. In: IC-AI, pp. 162–168 (2006)

  28. Kong, A., Zhang, D., Kamel, M.: Palmprint identification using feature-level fusion. Pattern Recogn. 39(3), 478–487 (2006)

    Article  MATH  Google Scholar 

  29. Rattani, A., Kisku, D.R., Bicego, M., Tistarelli, M.: September. Feature level fusion of face and fingerprint biometrics. In: First IEEE International Conference on Biometrics: Theory, Applications, and Systems, 2007. BTAS 2007, pp. 1–6. IEEE (2007)

  30. Zou, K.H., O’Malley, A.J., Mauri, L.: Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. Circulation 115(5), 654–657 (2007)

    Article  Google Scholar 

  31. Zweig, M.H., Campbell, G.: Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin. Chem. 39(4), 561–577 (1993)

    Article  Google Scholar 

  32. Agarwal, S., Chand, S.: Image forgery detection using multi scale entropy filter and local phase quantization. Int. J. Image Graph. Signal Process. 7(10), 78 (2015)

    Google Scholar 

  33. Muhammad, G., Al-Hammadi, M.H., Hussain, M., Bebis, G.: Image forgery detection using steerable pyramid transform and local binary pattern. Mach. Vis. Appl. 25(4), 985–995 (2014)

    Article  Google Scholar 

  34. He, Z., Lu, W., Sun, W., Huang, J.: Digital image splicing detection based on Markov features in DCT and DWT domain. Pattern Recogn. 45(12), 4292–4299 (2012)

    Article  Google Scholar 

  35. Liao, X., Yin, J., Guo, S., Li, X., Sangaiah, A.K.: Medical JPEG image steganography based on preserving inter-block dependencies. Comput. Electr. Eng. (2017). https://doi.org/10.1016/j.compeleceng.2017.08.020

  36. Samuel, O.W., Asogbon, G.M., Sangaiah, A.K., Fang, P., Li, G.: An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction. Expert Syst. Appl. 68, 163–172 (2017)

    Article  Google Scholar 

  37. Zhang, R., Shen, J., Wei, F., Li, X., Sangaiah, A.K.: Medical image classification based on multi-scale non-negative sparse coding. Artif. Intell. Med. (2017). https://doi.org/10.1016/j.artmed.2017.05.006

    Article  Google Scholar 

  38. Liang, W., Tang, M., Jing, L., Sangaiah, A.K., Huang, Y.: SIRSE: a secure identity recognition scheme based on electroencephalogram data with multi-factor feature. Comput. Electr. Eng. (2017). https://doi.org/10.1016/j.compeleceng.2017.05.001

  39. Samuel, O.W., Zhou, H., Li, X., Wang, H., Zhang, H., Sangaiah, A.K., Li, G.: Pattern recognition of electromyography signals based on novel time domain features for amputees’ limb motion classification. Comput. Electr. Eng. (2017). https://doi.org/10.1016/j.compeleceng.2017.04.003

  40. Arunkumar, N., Ramkumar, K., Venkatraman, V., Abdulhay, E., Fernandes, S.L., Kadry, S., Segal, S.: Classification of focal and non focal EEG using entropies. Pattern Recogn. Lett. 94, 112–117 (2017)

    Article  Google Scholar 

  41. Arunkumar, N., Kumar, K.R., Venkataraman, V.: Automatic detection of epileptic seizures using new entropy measures. J. Med. Imaging Health Inf. 6(3), pp. 724-730 (2016)

Download references

Author information

Authors and Affiliations

  1. Faculty of Computing, UniversitiTeknologi Malaysia (UTM), Skudai, 81310, Johor Bahru, Malaysia

    Araz Rajab Abrahim & Mohd Shafry Mohd Rahim

  2. IRDA Digital Media Center, Universiti Teknologi Malaysia (UTM), Skudai, 81310, Johor Bahru, Malaysia

    Mohd Shafry Mohd Rahim

  3. School Informatics and Applied Mathematics, Universiti Malaysia Terengganu (UMT), Terengganu, Malaysia

    Ghazali Bin Sulong

Authors
  1. Araz Rajab Abrahim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohd Shafry Mohd Rahim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ghazali Bin Sulong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Araz Rajab Abrahim.

Additional information

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s10586-022-03942-3

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abrahim, A.R., Rahim, M.S.M. & Sulong, G.B. RETRACTED ARTICLE: Splicing image forgery identification based on artificial neural network approach and texture features. Cluster Comput 22 (Suppl 1), 647–660 (2019). https://doi.org/10.1007/s10586-017-1668-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1668-8

Keywords

Search

Navigation