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International Workshop on Digital Watermarking

IWDW 2017: Digital Forensics and Watermarking pp 77–89Cite as

A Hybrid Feature Model for Seam Carving Detection

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Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10431))

Abstract

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.

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References

  1. Piva, A.: An overview on image forensics. ISRN Sig. Process. 2013, 22 (2013). Article ID 496701

    Google Scholar 

  2. Avidan, S., Shamir, A.: Seam carving for content-aware image resizing. ACM Trans. Graph. 26(3), 10 (2007)

    Article  Google Scholar 

  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. 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. Chang, W., Shih, T.K., Hsu, H.: Detection of seam carving in JPEG images. In: Proceedings of iCAST-UMEDIA (2013)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. Liu, Q.: An approach to detecting JPEG down-recompression and seam carving forgery under recompression anti-forensics. Pattern Recogn. 65, 35–46 (2016)

    Article  Google Scholar 

  10. Wei, J., Lin, Y., Wu, Y.: A patch analysis method to detect seam carved images. Pattern Recogn. Lett. 36, 100–106 (2014)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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

    Chapter  Google Scholar 

  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. 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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). http://www.csie.ntu.edu.tw/~cjlin/libsvm/

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA

    Jingyu Ye & Yun-Qing Shi

Authors
  1. Jingyu Ye
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  2. Yun-Qing Shi
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Corresponding author

Correspondence to Jingyu Ye .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Otto-von-Guericke University Magdeburg, Magdeburg, Germany

    Christian Kraetzer

  2. Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA

    Yun-Qing Shi

  3. Department of Computer Science, Otto-von-Guericke University Magdeburg, Magdeburg, Germany

    Jana Dittmann

  4. Graduate School Information Security, Korea University, Seoul, Korea (Republic of)

    Hyoung Joong Kim

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Ye, J., Shi, YQ. (2017). A Hybrid Feature Model for Seam Carving Detection. In: Kraetzer, C., Shi, YQ., Dittmann, J., Kim, H. (eds) Digital Forensics and Watermarking. IWDW 2017. Lecture Notes in Computer Science(), vol 10431. Springer, Cham. https://doi.org/10.1007/978-3-319-64185-0_7

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  • DOI: https://doi.org/10.1007/978-3-319-64185-0_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64184-3

  • Online ISBN: 978-3-319-64185-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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