A Hybrid Feature Model for Seam Carving Detection

  • Jingyu YeEmail author
  • Yun-Qing Shi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10431)


Seam carving, as a content-aware image resizing algorithm, is widely used nowadays. In this paper, an advanced hybrid feature model is presented to reveal the trace of seam carving, especially seam carving at a low carving rate, applied to uncompressed digital images. Two groups of features are designed to capture energy variation and pixel variation caused by seam carving, respectively. As indicated by the experimental works, the state-of-the-art performance on detecting 5% and 10% carving rate cases has been improved from 81.13% and 90.26% to 85.75% and 94.87%, respectively.


Seam carving detection Image forensics Local derivative pattern Markov transition probability Support vector machine 


  1. 1.
    Piva, A.: An overview on image forensics. ISRN Sig. Process. 2013, 22 (2013). Article ID 496701Google Scholar
  2. 2.
    Avidan, S., Shamir, A.: Seam carving for content-aware image resizing. ACM Trans. Graph. 26(3), 10 (2007)CrossRefGoogle Scholar
  3. 3.
    Sarkar, A., Nataraj, L., Manjunath, B.S.: Detection of seam carving and localization of seam insertions in digital images. In: Proceedings of the 11th ACM workshop on Multimedia and security, MM&Sec 2009, New York, NY, USA, pp. 107–116 (2009)Google Scholar
  4. 4.
    Fillion, C., Sharma, G.: Detecting content adaptive scaling of images for forensic applications. In: Media Forensics and Security. SPIE Proceedings, p. 75410. SPIE (2010)Google Scholar
  5. 5.
    Chang, W., Shih, T.K., Hsu, H.: Detection of seam carving in JPEG images. In: Proceedings of iCAST-UMEDIA (2013)Google Scholar
  6. 6.
    Wattanachote, K., Shih, T., Chang, W., Chang, H.: Tamper detection of JPEG image due to seam modification. IEEE Trans. Inf. Forensics Secur. 10(12), 2477–2491 (2015)CrossRefGoogle Scholar
  7. 7.
    Liu, Q., Chen, Z.: Improved approaches with calibrated neighboring joint density to steganalysis and seam-carved forgery detection in JPEG images. ACM Trans. Intell. Syst. Technol. 5(4), 63 (2014)Google Scholar
  8. 8.
    Liu, Q.: Exposing seam carving forgery under recompression attacks by hybrid large feature mining. In: 23rd International Conference on Pattern Recognition (ICPR), pp. 1036–1041 (2016)Google Scholar
  9. 9.
    Liu, Q.: An approach to detecting JPEG down-recompression and seam carving forgery under recompression anti-forensics. Pattern Recogn. 65, 35–46 (2016)CrossRefGoogle Scholar
  10. 10.
    Wei, J., Lin, Y., Wu, Y.: A patch analysis method to detect seam carved images. Pattern Recogn. Lett. 36, 100–106 (2014)CrossRefGoogle Scholar
  11. 11.
    Ryu, S., Lee, H., Lee, H.: Detecting trace of seam carving for forensic analysis. IEICE Trans. Inf. Syst. E97-D(5), 1304–1311 (2014)CrossRefGoogle Scholar
  12. 12.
    Yin, T., Yang, G., Li, L., Zhang, D., Sun, X.: Detecting seam carving based image resizing using local binary patterns. Comput. Secur. 55, 130–141 (2015)CrossRefGoogle Scholar
  13. 13.
    Lu, W., Wu, M.: Seam carving estimation using forensic hash. In: Proceedings of the Thirteenth ACM Multimedia Workshop on Multimedia and Security, MM&Sec 2011, New York, NY, USA, pp. 9–14 (2011)Google Scholar
  14. 14.
    Ye, J., Shi, Y.-Q.: A local derivative pattern based image forensic framework for seam carving detection. In: Shi, Y.Q., Kim, H.J., Perez-Gonzalez, F., Liu, F. (eds.) IWDW 2016. LNCS, vol. 10082, pp. 172–184. Springer, Cham (2016). doi: 10.1007/978-3-319-53465-7_13 CrossRefGoogle Scholar
  15. 15.
    Ye, J., Shi, Y.Q.: An effective method for seam carving detection. J. Inf. Secur. Appl. 35, 13–22 (2017). doi: 10.1016/j.jisa.2017.04.003 Google Scholar
  16. 16.
    Zhang, B., Gao, Y., Zhao, S., Liu, J.: Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Trans. Image Process. 19(2), 533–544 (2010)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefzbMATHGoogle Scholar
  18. 18.
    Schaefer, G., Stich, M.: UCID - an uncompressed colour image database. In: Storage and Retrieval Methods and Applications for Multimedia 2004. Proceedings of SPIE, vol. 5307, pp. 472–480 (2004)Google Scholar
  19. 19.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewarkUSA

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