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A joint source–channel coding approach to digital image self-recovery

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Abstract

The aim of the digital image self-embedding methods is to restore the content of a tampered image as much as possible. In this article, a joint source–channel coding solution is presented to solve this problem, in which an image compression algorithm is applied to generate a representation of the original image. The preserved information of the original image is used to produce hash bits that help the receiver to find the tampered area. Having the location of tampering known, it can be modeled as an erasure channel. Therefore, a channel coding phase is added to protect the image representation. The limited watermarking bit-budget will be optimally dedicated to the source and channel code bits based on the results derived by solving a dynamic programming optimization problem. It is shown in the experimental results that including this optimization stage results in the superiority of the proposed scheme to the similar state-of-the-art methods who lack such optimization.

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References

  1. Zhang, X., Wang, S.: Fragile watermarking with error-free restoration capability. IEEE Trans. Multimed. 10, 1490–1499 (2008)

    Article  Google Scholar 

  2. Qian, Z., Feng, G., Zhang, X., Wang, S.: Image self-embedding with high-quality restoration capability. Digit. Signal Process. 21, 278–286 (2011)

    Article  Google Scholar 

  3. Sarreshtedari, S., Akhaee, M.A.: A source-channel coding approach to digital image protection and self-recovery. IEEE Trans. Image Process. 24, 2266–2277 (2015)

    Article  MathSciNet  Google Scholar 

  4. Sarreshtedari, S., Akhaee, M.: Source-channel coding approach to generate tamper-proof images. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7435–7439 (2014)

  5. Sarreshtedari, S., Akhaee, M.A., Abbasfar, A.: Digital image self-recovery using unequal error protection. In: Signal Processing Conference (EUSIPCO), 2015 23rd European, pp. 71–75 (2015)

  6. Said, A., Pearlman, W.: A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Trans. Circuits Syst. Video Technol. 6, 243–250 (1996)

    Article  Google Scholar 

  7. Ke-kun, H.: Improved set partitioning in hierarchical trees algorithm based on adaptive coding order. J. Comput. Appl. 32, 732–735 (2012)

    Google Scholar 

  8. Blahut, R.: Algebraic fields, signal processing, and error control. Proce. IEEE 73, 874–893 (1985)

    Article  Google Scholar 

  9. Kim, J., Mersereau, R., Altunbasak, Y.: Error-resilient image and video transmission over the internet using unequal error protection. IEEE Trans. Image Process. 12, 121–131 (2003)

    Article  Google Scholar 

  10. Arslan, S., Cosman, P., Milstein, L.: Concatenated block codes for unequal error protection of embedded bit streams. IEEE Trans. Image Process. 21, 1111–1122 (2012)

    Article  MathSciNet  Google Scholar 

  11. Chande, V., Farvardin, N.: Progressive transmission of images over memoryless noisy channels. IEEE J. Sel. Areas Commun. 18, 850–860 (2000)

    Article  Google Scholar 

  12. Gloe, T., Bhme, R.: The dresden image database for benchmarking digital image forensics. J. Digit. Forensic Pract. 3, 150–159 (2010)

    Article  Google Scholar 

  13. Zhang, X., Wang, S., Qian, Z., Feng, G.: Reference sharing mechanism for watermark self-embedding. IEEE Trans. Image Process. 20, 485–495 (2011)

    Article  MathSciNet  Google Scholar 

  14. Zhang, X., Qian, Z., Ren, Y., Feng, G.: Watermarking with flexible self-recovery quality based on compressive sensing and compositive reconstruction. IEEE Trans. Inf. Forensics Secur. 6, 1223–1232 (2011)

    Article  Google Scholar 

  15. Korus, P., Dziech, A.: Efficient method for content reconstruction with self-embedding. IEEE Trans. Image Process. 22, 1134–1147 (2013)

    Article  MathSciNet  Google Scholar 

  16. Dadkhah, S., Manaf, A.A., Hori, Y., Hassanien, A.E., Sadeghi, S.: An effective SVD-based image tampering detection and self-recovery using active watermarking. Signal Process.: Image Commun. 29, 1197–1210 (2014)

    Google Scholar 

  17. Li, C., Wang, Y., Ma, B., Zhang, Z.: A novel self-recovery fragile watermarking scheme based on dual-redundant-ring structure. Comput. Electr. Eng. 37, 927–940 (2011)

    Article  Google Scholar 

  18. Bas, P., Furon, T.: The Dataset from the 2nd Bows Contest. (2012, Mar. 26) [Online]. http://bows2.ec-lille.fr/

  19. Bas, P., Furon, T.: The Dataset from the 2nd Bows Contest. (2012, Mar. 26). http://bows2.ec-lille.fr/

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Correspondence to Saeed Sarreshtedari.

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Sarreshtedari, S., Abbasfar, A. & Akhaee, M.A. A joint source–channel coding approach to digital image self-recovery. SIViP 11, 1371–1378 (2017). https://doi.org/10.1007/s11760-017-1095-6

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  • DOI: https://doi.org/10.1007/s11760-017-1095-6

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