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Call for Paper - September – 2022 Edition   

(SJIF Impact Factor: 5.966) (IJIFACTOR 3.8, RANKING: A+) (PIF: 3.460)

IJATCA solicits original research papers for the September – 2022 Edition.
Last date of manuscript submission is September 30, 2022.


Detection of Splicing in Low Contrast Digital Images using Noise Based Features

Volume: 9 Issue: 1
Year of Publication: 2022
Authors: Amandeep Kaur, Navdeep Kanwal, Lakhwinder Kaur


The main issue in image forensics is to discover whether an image is authentic or forged and, if forged, to locate which regions have been manipulated. The simple accessibility of image manipulation software have proliferated the possibility of image forgery. Detection of Splicing forgery is targeted in this paper. Noise component of a color image have been utilized to extract features from suspected image. As the consistency of noise between RGB color channel of forged and authentic image are different, so it leaves the clues of forgery. First digit features are extracted using Benfords law and provided to the SVM classifier Columbia uncompressed image splicing detection evaluation dataset and CASIA v1.0 dataset are used to test the proposed technique. Our technique outperforms various previous techniques of image splicing detection.


  1. C. Kaur and N. Kanwal, "An Analysis of Image Forgery Detection Techniques," vol. 7, no. June, pp. 486-500, 2019, doi: 10.19139/soic.v7i2.542.

  2. A. Piva, "An Overview on Image Forensics," vol. 2013, 2013.

  3. S. K. Mankar and P. A. A. Gurjar, "Image Forgery Types and Their Detection : A Review," vol. 5, no. 4, pp. 174-178, 2015.

  4. H. Farid, "Creating and Detecting Doctored and Virtual Images : Implications to The Child Pornography Prevention Act," no. 2000.

  5. G. K. Birajdar and V. H. Mankar, "Digital image forgery detection using passive techniques: A survey," Digit. Investig., vol. 10, no. 3, pp. 226-245, 2013, doi: 10.1016/j.diin.2013.04.007.

  6. K. Asghar, Z. Habib, and M. Hussain, "Copy-move and splicing image forgery detection and localization techniques : a review," Aust. J. Forensic Sci., vol. 0618, pp. 1-27, 2017, doi: 10.1080/00450618.2016.1153711.

  7. J. Luk and J. Fridrich, "Estimation of Primary Quantization Matrix in Double Compressed JPEG Images."

  8. J. Goo, H. Tae, H. Park, Y. Ho, M. Il, and K. Eom, "Quantization-based Markov feature extraction method for image splicing detection," Mach. Vis. Appl., 2018, doi: 10.1007/s00138-018-0911-5.

  9. S. Milani, M. Tagliasacchi, S. Tubaro, S. Milani, M. Tagliasacchi, and I. Processing, "APSIPA Transactions on Signal and Information Processing Transactions on Signal and Information Processing : Discriminating multiple JPEG compressions using  rst digit features Discriminating multiple JPEG compressions," no. December 2014, pp. 0-10, 2015, doi: 10.1017/ATSIP.2014.19.

  10. X. Pan, "Exposing Image Forgery with Blind Noise Estimation," pp. 15-20, 2011.

  11. "TOWARDS LEARNED COLOR REPRESENTATIONS FOR IMAGE SPLICING DETECTION Benjamin Hadwiger , Daniele Baracchi , Alessandro Piva , Christian Riess urnberg Dipartimento di Ingegneria dell Informazione , Universit `," pp. 8281-8285, 2019.

  12. T. Ng, S. Chang, and Q. Sun, "Blind detection of photomontage using higher order statistics," 1845.

  13. R. Salloum, Y. Ren, and C. J. Kuo, "Image Splicing Localization Using A Multi-Task Fully Convolutional Network ( MFCN ) arXiv : 1709 . 02016v1 [ cs . CV ] 6 Sep 2017," pp. 1-19, 2017.

  14. A. M. Das and S. Aji, A Fast and Efficient Method for Image Splicing Localization Using BM3D Noise Estimation. Springer Singapore.

  15. N. Thanh, P. J. Lee, and G. K. C. Park, "Efficient image splicing detection algorithm based on markov features," 2018.

  16. P. S. Abhijith and P. Simon, Improved Blurred Image Splicing Localization with KNN Matting. Springer Singapore, 2019.

  17. A. K. Jaiswal and R. Srivastava, "A technique for image splicing detection using hybrid feature set," Multimed. Tools Appl., vol. 79, no. 17-18, pp. 11837-11860, 2020, doi: 10.1007/s11042-019-08480-6.

  18. N. Y. Hussien, R. O. Mahmoud, and H. H. Zayed, "Deep learning on digital image splicing detection using cfa artifacts," Int. J. Sociotechnology Knowl. Dev., vol. 12, no. 2, pp. 31-44, 2020, doi: 10.4018/IJSKD.2020040102.

  19. C. Destruel, V. Itier, O. Strauss, and W. Puech, "Color noise-based feature for splicing detection and localization," 2018 IEEE 20th Int. Work. Multimed. Signal Process., pp. 1-6.

  20. J. A. Redi, W. Taktak, and J. Dugelay, "Digital image forensics : a booklet for beginners," no. October 2010, pp. 133-162, 2011, doi: 10.1007/s11042-010-0620-1.

  21. B. Patil, M. E. Student, S. Chapaneri, and D. Jayaswal, "Improved Image Splicing Forgery Localization with First Digits and Markov Model Features," 2017.


  23. P. O. Box, "CASIA IMAGE TAMPERING DETECTION EVALUATION DATABASE Jing Dong , Wei Wang and Tieniu Tan Institute of Automation , Chinese Academy of Sciences," pp. 422-426, 2013.

  24. D. Cozzolino, G. Poggi, and L. Verdoliva, "Splicebuster: A new blind image splicing detector," 2015 IEEE Int. Work. Inf. Forensics Secur. WIFS 2015 - Proc., no. November, 2015, doi: 10.1109/WIFS.2015.7368565.

  25. S. Lyu, X. Pan, and X. Zhang, "Exposing Region Splicing Forgeries with Blind Local Noise Estimation," Int. J. Comput. Vis., vol. 110, no. 2, pp. 202-221, 2013, doi: 10.1007/s11263-013-0688-y.

  26. P. Ferrara, T. Bianchi, A. De Rosa, A. Piva, and S. Member, "Image Forgery Localization via Fine-Grained Analysis of CFA Artifacts," vol. 7, no. 5, pp. 1566-1577, 2012.

  27. B. Mahdian and S. Saic, "Using noise inconsistencies for blind image forensics," Image Vis. Comput., vol. 27, no. 10, pp. 1497-1503, 2009, doi: 10.1016/j.imavis.2009.02.001.

  28. J. V. C. I. R, C. Pun, B. Liu, and X. Yuan, "Multi-scale noise estimation for image splicing forgery detection q," J. Vis. Commun. Image Represent., vol. 38, pp. 195-206, 2016, doi: 10.1016/j.jvcir.2016.03.005.

  29. J. Dong, W. Wang, T. Tan, and Y. Q. Shi, "Run-Length and Edge Statistics Based Approach for Image Splicing Detection Run-length and edge statistics based approach for image splicing detection," no. April 2014, pp. 0-12, 2008, doi: 10.1007/978-3-642-04438-0.

  30. W. Chen, Y. Q. Shi, and W. Su, "Image splicing detection using 2-D phase congruency and statistical moments of characteristic function," vol. 6505, pp. 1-8, 2017.

  31. Z. He, W. Sun, W. Lu, and H. Lu, "Digital image splicing detection based on approximate run length," Pattern Recognit. Lett., vol. 32, no. 12, pp. 1591-1597, 2011, doi: 10.1016/j.patrec.2011.05.013.


Forgery Detection, Image Forensics, Low Contrast, Splicing Forgery.

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