ETRI Journal

  • 제43권1호
  • /
  • Pages.109-119
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  • 2021
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  • 1225-6463(pISSN)
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  • 2233-7326(eISSN)
한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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Region-based scalable self-recovery for salient-object images

  • Daneshmandpour, Navid (Department of Electrical and Electronics Engineering, Shiraz University of Technology) ;
  • Danyali, Habibollah (Department of Electrical and Electronics Engineering, Shiraz University of Technology) ;
  • Helfroush, Mohammad Sadegh (Department of Electrical and Electronics Engineering, Shiraz University of Technology)
  • 투고 : 2018.11.23
  • 심사 : 2020.03.24
  • 발행 : 2021.02.01
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초록

Self-recovery is a tamper-detection and image recovery methods based on data hiding. It generates two types of data and embeds them into the original image: authentication data for tamper detection and reference data for image recovery. In this paper, a region-based scalable self-recovery (RSS) method is proposed for salient-object images. As the images consist of two main regions, the region of interest (ROI) and the region of non-interest (RONI), the proposed method is aimed at achieving higher reconstruction quality for the ROI. Moreover, tamper tolerability is improved by using scalable recovery. In the RSS method, separate reference data are generated for the ROI and RONI. Initially, two compressed bitstreams at different rates are generated using the embedded zero-block coding source encoder. Subsequently, each bitstream is divided into several parts, which are protected through various redundancy rates, using the Reed-Solomon channel encoder. The proposed method is tested on 10 000 salient-object images from the MSRA database. The results show that the RSS method, compared to related methods, improves reconstruction quality and tamper tolerability by approximately 30% and 15%, respectively.

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참고문헌

  1. W.-L. L. Tai and Z.-J. J. Liao, Image self-recovery with watermark self-embedding, Signal Process. Image Commun. 65 (2018), 11-25. https://doi.org/10.1016/j.image.2018.03.011
  2. D. Singh and S. K. Singh, DCT based efficient fragile watermarking scheme for image authentication and restoration, Multimed. Tools Applicat. 76 (2017), no. 1, 953-977. https://doi.org/10.1007/s11042-015-3010-x
  3. B. B. Haghighi, A. H. Taherinia, and A. H. Mohajerzadeh, TRLG: Fragile blind quad watermarking for image tamper detection and recovery by providing compact digests with quality optimized using LWT and GA, Inf. Sci. 486 (2018), 204-230. https://doi.org/10.1016/j.ins.2019.02.055
  4. O. Benrhouma, H. Hermassi, and S. Belghith, Security analysis and improvement of an active watermarking system for image tampering detection using a self-recovery scheme, Multimed. Tools Applicat. 76 (2017), no. 20, 21133-21156. https://doi.org/10.1007/s11042-016-4054-2
  5. D. Singh and S. K. Singh, Effective self-embedding watermarking scheme for image tampered detection and localization with recovery capability, J. Vis. Commun. Image Represent. 38 (2016), 775-789. https://doi.org/10.1016/j.jvcir.2016.04.023
  6. K. Sreenivas and V. Kamakshiprasad, Improved image tamper localisation using chaotic maps and self-recovery, J. Vis. Commun. Image Represent. 49 (2017), 164-176. https://doi.org/10.1016/j.jvcir.2017.09.001
  7. X. Zhang et al., Reference sharing mechanism for watermark self-embedding, IEEE Trans. Image Process. 20 (2011), no. 2, 485-495. https://doi.org/10.1109/TIP.2010.2066981
  8. X. Zhang et al., Watermarking with flexible self-recovery quality based on compressive sensing and compositive reconstruction, IEEE Trans. Inf. Forensics Secur. 6 (2011), no. 4, 1223-1232. https://doi.org/10.1109/TIFS.2011.2159208
  9. X. Zhang et al., Self-embedding watermark with flexible restoration quality, Multimed. Tools Applicat. 54 (2011), no. 2, 385-395. https://doi.org/10.1007/s11042-010-0541-z
  10. Z. Qian et al., Image self-embedding with high-quality restoration capability, Digit. Signal Process. 21 (2011), no. 2, 278-286. https://doi.org/10.1016/j.dsp.2010.04.006
  11. Y. Huo, H. He, and F. Chen, Alterable-capacity fragile watermarking scheme with restoration capability, Opt. Commun. 285 (2012), no. 7, 1759-1766. https://doi.org/10.1016/j.optcom.2011.12.044
  12. C. Qin, C.-C. Chang, and K.-N. Chen, Adaptive self-recovery for tampered images based on VQ indexing and inpainting, Signal Process. 93 (2013), no. 4, 933-946. https://doi.org/10.1016/j.sigpro.2012.11.013
  13. C. Qin, C.-C. Chang, and P.-Y. Chen, Self-embedding fragile watermarking with restoration capability based on adaptive bit allocation mechanism, Signal Process. 92 (2012), no. 4, 1137-1150. https://doi.org/10.1016/j.sigpro.2011.11.013
  14. P. Korus and A. Dziech, Adaptive self-embedding scheme with controlled reconstruction performance, IEEE Trans. Inf. Forensics Secur. 9 (2014), no. 2, 169-181. https://doi.org/10.1109/TIFS.2013.2295154
  15. J. M. Zain and A. R. M. Fauzi, Medical image watermarking with tamper detection and recovery, in Proc. Int. Conf. IEEE Eng. Med. Biol. Soc. (New York, USA), Aug. 2006, pp. 3270-3273.
  16. S. Wang and S. Tsai, Automatic image authentication and recovery using fractal code embedding and image inpainting, Pattern Recognit. 41 (2008), no. 2, 701-712. https://doi.org/10.1016/j.patcog.2007.05.012
  17. C. Cruz-Ramos et al., Image authentication scheme based on self-embedding watermarking, in Proc. Iberoamer. Congr. Pattern Recognit. (Guadalajara, Mexico), Nov. 2009, pp. 1005-1012.
  18. K.-S. Kim et al., Region-based tampering detection and recovery using homogeneity analysis in quality-sensitive imaging, Comput. Vis. Image Underst. 115 (2011), no. 9, 1308-1323. https://doi.org/10.1016/j.cviu.2011.05.001
  19. R. Eswaraiah and E. S. Reddy, Medical image watermarking technique for accurate tamper detection in ROI and exact recovery of ROI, Int. J. Telemed. Appl. 2014 (2014), 1-10.
  20. P. Korus and A. Dziech, Efficient method for content reconstruction with self-embedding, IEEE Trans. Image Process. 22 (2013), no. 3, 1134-1147. https://doi.org/10.1109/TIP.2012.2227769
  21. P. Korus, J. Bialas, and A. Dziech, Towards practical self-embedding for JPEG-compressed digital images, IEEE Trans. Multimed. 17 (2015), no. 2, 157-170. https://doi.org/10.1109/TMM.2014.2368696
  22. S. Sarreshtedari and M. A. Akhaee, A source-channel coding approach to digital image protection and self-recovery, IEEE Trans. Image Process. 24 (2015), no. 7, 2266-2277. https://doi.org/10.1109/TIP.2015.2414878
  23. S. Sarreshtedari, A. Abbasfar, and M. Ali, A joint source - channel coding approach to digital image self-recovery, Signal Image Video Process. 11 (2017), no. 7, 1371-1378. https://doi.org/10.1007/s11760-017-1095-6
  24. N. Daneshmandpour, H. Danyali, and M. S. Helfroush, Scalable image self-embedding based on dual-rate SPIHT-LDPC reference generation scheme, Radioeng. 28 (2019), no. 1, 199-206.
  25. N. Daneshmandpour, H. Danyali, and M. S. Helfroush, Multi-rate reference embedding for highly-scalable self-recovery using fuzzy mamdani, J. Intell. Fuzzy Syst. 37 (2019), no. 6, 1-11.
  26. T. Liu et al., Learning to detect a salient object, IEEE Trans. Pattern Anal. Mach. Intell. 33 (2011), no. 2, 353-367. https://doi.org/10.1109/TPAMI.2010.70
  27. A. Said and W. Pearlman, A new, fast, and efficient image codec based on set partitioning in hierarchical trees, IEEE Trans. Circuits Syst. Video Technol. 6 (1996), no. 3, 243-250. https://doi.org/10.1109/76.499834
  28. J. Shapiro, Embedded image coding using zerotrees of wavelet coefficients, IEEE Trans. Signal Process. 41 (1993), no. 12, 3445-3462. https://doi.org/10.1109/78.258085
  29. S. T. Hsiang, Embedded image coding using zeroblocks of subband/wavelet coefficients and context modeling, in Proc. DCC 2001. Data Comp. Conf. IEEE Comput. Soc. (Snowbird, UT, USA), Mar. 2001, pp. 83-92.