Fragile watermarking for image authentication using dyadic walsh ordering

(1) * Prajanto Wahyu Adi Mail (Universitas Diponegoro, Indonesia)
(2) Adi Wibowo Mail (Universitas Diponegoro, Indonesia)
(3) Guruh Aryotejo Mail (Universitas Diponegoro, Indonesia)
(4) Ferda Ernawan Mail (Universiti Malaysia Pahang, Malaysia)
*corresponding author

Abstract


A digital image is subjected to the most manipulation. This is driven by the easy manipulating process through image editing software which is growing rapidly. These problems can be solved through the watermarking model as an active authentication system for the image. One of the most popular methods is Singular Value Decomposition (SVD) which has good imperceptibility and detection capabilities. Nevertheless, SVD has high complexity and can only utilize one singular matrix S, and ignore two orthogonal matrices. This paper proposes the use of the Walsh matrix with dyadic ordering to generate a new S matrix without the orthogonal matrices. The experimental results showed that the proposed method was able to reduce computational time by 22% and 13% compared to the SVD-based method and similar methods based on the Hadamard matrix respectively. This research can be used as a reference to speed up the computing time of the watermarking methods without compromising the level of imperceptibility and authentication.

Keywords


Watermarking; Dyadic Walsh matrix; Active tampering detection; Image authentication

   

DOI

https://doi.org/10.26555/ijain.v9i3.1017
      

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References


[1] M. Rezaei and H. Taheri, “Digital image self-recovery using CNN networks,” Optik (Stuttg)., vol. 264, no. 1, pp. 1–12, 2022, doi: 10.1016/j.ijleo.2022.169345.

[2] J. Chen, X. Liao, and Z. Qin, “Identifying tampering operations in image operator chains based on decision fusion,” Signal Process. Image Commun., vol. 95, no. April, p. 116287, 2021, doi: 10.1016/j.image.2021.116287.

[3] J. V. C. I. R, L. Zheng, Y. Zhang, and V. L. L. Thing, “A survey on image tampering and its detection in real-world photos q,” J. Vis. Commun. Image Represent., vol. 58, pp. 380–399, 2019, doi: 10.1016/j.jvcir.2018.12.022.

[4] W. D. Ferreira, C. B. R. Ferreira, G. da Cruz Júnior, and F. Soares, “A review of digital image forensics,” Comput. Electr. Eng., vol. 85, pp. 1–9, 2020, doi: 10.1016/j.compeleceng.2020.106685.

[5] H. M. Al-Otum and A. A. A. Ellubani, “Secure and effective color image tampering detection and self restoration using a dual watermarking approach,” Optik (Stuttg)., vol. 262, pp. 1–22, 2022, doi: 10.1016/j.ijleo.2022.169280.

[6] A. Aminuddin and F. Ernawan, “AuSR1: Authentication and self-recovery using a new image inpainting technique with LSB shifting in fragile image watermarking,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no.8, pp. 5822-5840, 2022, doi: 10.1016/j.jksuci.2022.02.009.

[7] P. Johnston and E. Elyan, “A review of digital video tampering : From simple editing to full synthesis,” Digit. Investig., vol. 29, pp. 67–81, 2019, doi: 10.1016/j.diin.2019.03.006.

[8] M. Raveendra and K. Nagireddy, “Tamper video detection and localization using an adaptive segmentation and deep network technique,” J. Vis. Commun. Image Represent., vol. 82, pp. 1–13, 2022, doi: 10.1016/j.jvcir.2021.103401.

[9] K. Sitara and B. M. Mehtre, “Digital video tampering detection : An overview of passive techniques,” Digit. Investig., vol. 18, pp. 8–22, 2016, doi: 10.1016/j.diin.2016.06.003.

[10] F. Ding, G. Zhu, W. Dong, and Y. Shi, “An efficient weak sharpening detection method for image forensics,” J. Vis. Commun. Image Represent., vol. 50, pp. 93–99, 2018, doi: 10.1016/j.jvcir.2017.11.009.

[11] J. Sun, S. Kim, S. Lee, and S. Ko, “A novel contrast enhancement forensics based on convolutional neural networks,” Signal Process. Image Commun., vol. 63, pp. 149–160, 2018, doi: 10.1016/j.image.2018.02.001.

[12] D. Bhardwaj and V. Pankajakshan, “A JPEG blocking artifact detector for image forensics,” Signal Process. Image Commun., vol. 68, pp. 155–161, 2018, doi: 10.1016/j.image.2018.07.011.

[13] X. Wang, Q. Zhang, C. Jiang, and J. Xue, “Perceptual hash-based coarse-to-fine grained image tampering forensics method,” J. Vis. Commun. Image Represent., vol. 78, pp. 1–15, 2021, doi: 10.1016/j.jvcir.2021.103124.

[14] M. Jana, B. Jana, and S. Joardar, “Local feature based self-embedding fragile watermarking scheme for tampered detection and recovery utilizing AMBTC with fuzzy logic,” J. King Saud Univ. - Comput. Inf. Sci., pp. 1–14, 2022, doi: 10.1016/j.jksuci.2021.12.011.

[15] B. Bolourian Haghighi, A. H. Taherinia, and A. H. Mohajerzadeh, “TRLG: Fragile blind quad watermarking for image tamper detection and recovery by providing compact digests with optimized quality using LWT and GA,” Inf. Sci. (Ny)., vol. 486, pp. 204–230, 2019, doi: 10.1016/j.ins.2019.02.055.

[16] A. Aminuddin and F. Ernawan, “AuSR2: Image watermarking technique for authentication and self-recovery with image texture preservation,” Comput. Electr. Eng., vol. 102, pp. 1–17, 2022, doi: 10.1016/J.COMPELECENG.2022.108207.

[17] J. Yang, T. Huang, and L. Su, “Using similarity analysis to detect frame duplication forgery in videos,” Multimed. Tools Appl., vol. 75, no. 4, pp. 1793–1811, 2016, doi: 10.1007/s11042-014-2374-7.

[18] G. Cattaneo, G. Roscigno, and U. Ferraro Petrillo, “Improving the experimental analysis of tampered image detection algorithms for biometric systems,” Pattern Recognit. Lett., vol. 113, pp. 93–101, 2018, doi: 10.1016/j.patrec.2017.01.006.

[19] X. Yuan, X. Li, and T. Liu, “Gauss–Jordan elimination-based image tampering detection and self-recovery,” Signal Process. Image Commun., vol. 90, no. April 2020, p. 116038, 2021, doi: 10.1016/j.image.2020.116038.

[20] S. N. V. J. Devi Kosuru, G. Swain, N. Kumar, and A. Pradhan, “Image tamper detection and correction using Merkle tree and remainder value differencing,” Optik (Stuttg)., vol. 261, no. January, p. 169212, 2022, doi: 10.1016/j.ijleo.2022.169212.

[21] Y. Wang, X. Kang, and Y. Chen, “Robust and accurate detection of image copy-move forgery using PCET-SVD and histogram of block similarity measures,” J. Inf. Secur. Appl., vol. 54, pp. 1–11, 2020, doi: 10.1016/j.jisa.2020.102536.

[22] Y. Xiang, D. Xiao, H. Wang, and X. Li, “A secure image tampering detection and self-recovery scheme using POB number system over cloud,” Signal Processing, vol. 162, pp. 282–295, 2019, doi: 10.1016/j.sigpro.2019.04.022.

[23] Q. Kang, K. Li, and H. Chen, “An SVD-based Fragile Watermarking Scheme With Grouped Blocks,” in International Conference on Information Technology and Electronic Commerce, 2014, pp. 172–179, 2015, doi: 10.1109/ICITEC.2014.7105595.

[24] P. W. Adi and P. Arsiwi, “Fast and Robust Watermarking Method using Walsh Matrix Partition,” 2019 2nd Int. Semin. Res. Inf. Technol. Intell. Syst. ISRITI 2019, pp. 468–472, 2019, doi: 10.1109/ISRITI48646.2019.9034627.

[25] F. Ernawan, P. W. Adi, S. C. Liew, E. A. Sarwoko, and E. Winarno, “Fast image watermarking based on signum of cosine matrix,” Indones. J. Electr. Eng. Comput. Sci., vol. 25, no. 3, pp. 1383–1391, 2022, doi: 10.11591/ijeecs.v25.i3.pp1383-1391.

[26] K. Prabha and I. Shatheesh Sam, “An effective robust and imperceptible blind color image watermarking using WHT,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 6, pp. 2982–2992, 2022, doi: 10.1016/j.jksuci.2020.04.003.

[27] J. Du, Z. Chen, S. Fu, L. Qu, and C. Li, “Constructions of 2-resilient rotation symmetric Boolean functions through symbol transformations of cyclic Hadamard matrix,” Theor. Comput. Sci., vol. 919, pp. 80–91, 2022, doi: 10.1016/j.tcs.2022.03.033.

[28] N. Budda, K. Meenakshi, P. Kora, G. V. Subba Reddy, and K. Swaraja, “Image Digest using Color Vector Angle and Dominant Walsh-Hadamard Transform Coefficients,” Mater. Today Proc., no. xxxx, 2021, doi: 10.1016/j.matpr.2020.11.488.

[29] A. Sergeev and A. Vostrikov, “Calculating symmetrical Hadamard matrices of Balonin-Seberry construction for coding and masking,” Procedia Comput. Sci., vol. 176, pp. 1722–1728, 2020, doi: 10.1016/j.procs.2020.09.197.

[30] J.-L. Baril, S. Kirgizov, and V. Vajnovszki, “Gray codes for Fibonacci q-decreasing words,” Theor. Comput. Sci., vol. 927, pp. 120–132, 2022, doi: 10.1016/j.tcs.2022.06.003.

[31] J. PejaS and L. Cierocki, “Reversible data hiding scheme for images using gray code pixel value optimization,” Procedia Comput. Sci., vol. 192, pp. 328–337, 2021, doi: 10.1016/j.procs.2021.08.034.

[32] P. W. Adi and P. Arsiwi, “A novel watermarking method using hadamard matrix quantization,” J. ICT Res. Appl., vol. 14, no. 1, pp. 1–15, 2020, doi: 10.5614/itbj.ict.res.appl.2020.14.1.1.

[33] R. Y. Abadi and P. Moallem, “Robust and optimum color image watermarking method based on a combination of DWT and DCT,” Optik (Stuttg)., vol. 261, pp. 1–17, 2022, doi: 10.1016/j.ijleo.2022.169146.

[34] W. Wan, J. Wang, Y. Zhang, J. Li, H. Yu, and J. Sun, “A comprehensive survey on robust image watermarking,” Neurocomputing, vol. 488, pp. 226–247, 2022, doi: 10.1016/j.neucom.2022.02.083.




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