An Image Splicing Localization Algorithm Based on SLIC and Image Features

  • Haipeng ChenEmail author
  • Chaoran Zhao
  • Zenan Shi
  • Fuxiang Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11166)


Aiming at the problem of low accuracy, high computational complexity and incomplete edge information of most image splicing localization algorithm, this paper proposes a new image splicing localization algorithm. First, the SLIC image segmentation algorithm is used to segment the image. Secondly, the noise estimation value of each super-pixel block is calculated by the FAST noise estimation algorithm. Then, weight of each image block is calculated through noise and image features. Finally, the noise value sequence is processed by clustering and statistical processing to determine the pixels of the background area and the splicing area, thus the splicing area is located. In this paper, the algorithm is tested on the color image database of Columbia, and compared with the existing image splicing localization algorithms based on block-segmentation and based on pixel. The experiment shows that the proposed algorithm can preserve the connection between image features, hold the edge of the splicing area, and effectively improve the efficiency of localization detection under the premise of ensuring the accuracy of image splicing localization.


Image splicing Image splicing localization Super-pixels Image features Noise estimation 


  1. 1.
    Walia, S., Kumar, K.: Digital image forgery detection: a systematic scrutiny. Aust. J. Forensic Sci. 1, 1–39 (2018). Scholar
  2. 2.
    Zhang, L., Yang, Y., Wang, M.: Detecting densely distributed graph patterns for fine-grained image categorization. IEEE Trans. Image Process. 25(2), 553–565 (2016). Scholar
  3. 3.
    Ng, T.T., Chang, S.F., Sun, Q. (2004) Blind detection of photomontage using higher order statistics. In: Proceedings - IEEE International Symposium on Circuits and Systems, vol. 5, pp. 688–691.
  4. 4.
    Zhang, L., Gao, Y., Hong, R., Hu, Y., Ji, R., Dai, Q.: Probabilistic skimlets fusion for summarizing multiple consumer landmark videos. IEEE Trans. Multimed. 17(1), 40–49 (2014). Scholar
  5. 5.
    Agarwal, S., Chand, S.: Image forgery detection using multi scale entropy filter and local phase quantization. Int. J. Image Graph. Sig. Process. 7(10), 78–85 (2015). Scholar
  6. 6.
    Zhang, L., Hong, R., Gao, Y., Ji, R., Dai, Q., Li, X.: Image categorization by learning a propagated graphlet path. IEEE Trans. Neural Netw. Learn. Syst. 27(3), 674–685 (2017). Scholar
  7. 7.
    Hashmi, M.F., Keskar, A.G.: Image forgery authentication and classification using hybridization of HMM and SVM classifier. Int. J. Secur. Appl. 9(4), 125–140 (2015). Scholar
  8. 8.
    Vaishnavi, D., Subashini, T.S.: Recognizing image splicing forgeries using histogram features. In: MEC International Conference on Big Data and Smart City, pp. 1–4. IEEE, New York (2016).
  9. 9.
    Wang, B., Kong, X.: Image splicing localization based on re-demosaicing. In: Zeng, D. (ed.) Advances in Information Technology and Industry Applications. LNEE, vol. 136, pp. 725–732. Springer, Heidelberg (2012). Scholar
  10. 10.
    Zampoglou, M., Papadopoulos, S., Kompatsiaris, Y.: Large-scale evaluation of splicing localization algorithms for web images. Multimed. Tools Appl. 76, 1–34 (2016). Scholar
  11. 11.
    Bahrami, K., Kot, A.C., Li, L., et al.: Blurred image splicing localization by exposing blur type inconsistency. IEEE Trans. Inf. Forensics Secur. 10(5), 999–1009 (2015). Scholar
  12. 12.
    Immerkær, J.: Fast noise variance estimation. Comput. Vis. Image Underst. 64(2), 300–302 (1996). Scholar
  13. 13.
    Lyu, S., Pan, X., Zhang, X.: Exposing region splicing forgeries with blind local noise estimation. Int. J. Comput. Vis. 110(2), 202–221 (2014). Scholar
  14. 14.
    Mahdian, B., Saic, S.: Using noise inconsistencies for blind image forensics. Image Vis. Comput. 27(10), 1497–1503 (2009). Scholar
  15. 15.
    Pan, X., Zhang, X., Lyu, S.: Exposing image forgery with blind noise estimation. In: Thirteenth ACM Multimedia Workshop on Multimedia and Security, pp. 15–20. ACM, New York (2011).
  16. 16.
    Pan, X., Zhang, X., Lyu, S.: Exposing image splicing with inconsistent local noise variances. In: IEEE International Conference on Computational Photography, pp 1–10. IEEE, New York (2012).
  17. 17.
    Achanta, R., Shaji, A., Smith, K., et al.: SLIC superpixels compared to state-of-the-art super-pixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 234(11), 2274–2282 (2012). Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Haipeng Chen
    • 1
    Email author
  • Chaoran Zhao
    • 1
  • Zenan Shi
    • 1
  • Fuxiang Zhu
    • 2
  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.Lucion Technology Corp., Ltd.JinanChina

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