Implementation of SLT-DCT Steganography Method on Images to Improve the Quality of Steganography Images

Authors

  • Ramadani Ritonga Universitas Potensi Utama Author
  • Rika Rosnelly Universitas Potensi Utama Author
  • Wanayumini Wanayumini Universitas Asahan Author

DOI:

https://doi.org/10.30743/qad1k130

Keywords:

Steganography; Image Processing; SLT; DCT; PSNR; MSE

Abstract

The study explores the implementation of a hybrid Slantlet Transform (SLT) and Discrete Cosine Transform (DCT) in the context of image steganography, focusing on enhancing the quality of stego images while maintaining a high level of imperceptibility. One of the main challenges in steganography is embedding secret messages into images without compromising their visual quality. In this research, the combination of SLT and DCT is applied to embed secret messages into cover images, with the hope that this method can preserve visual quality. The quality of the stego images is evaluated using two main metrics: Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE). PSNR is used as a measure to assess how well the steganographic image maintains high visual quality, while MSE serves to measure the difference between the original and stego images. Experimental results show that the hybrid SLT-DCT method significantly produces higher PSNR values compared to the individual implementation of SLT or DCT. This indicates that the combination of these two methods not only ensures better image quality but also enhances the security of the embedded message. Thus, this approach makes a significant contribution to the development of more efficient and effective steganography techniques, offering a solution that meets the need for high visualization while maintaining the integrity of the hidden data. The success of this method paves the way for further research in the field of steganography to explore other transformation combinations that can improve the results obtained.

References

Z. Wang, M. Zhou, B. Liu, and T. Li, “Deep Image Steganography Using Transformer and Recursive Permutation,” Entropy, vol. 24, no. 7, 2022, doi: 10.3390/e24070878.

Rahul and Jyoti, “Image Steganography: A Review,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 11, no. 12, 2023, doi: 10.22214/ijraset.2023.57655.

X. Liao, J. Yin, M. Chen, and Z. Qin, “Adaptive Payload Distribution in Multiple Images Steganography Based on Image Texture Features,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 2, 2022, doi: 10.1109/TDSC.2020.3004708.

X. Duan, D. Guo, N. Liu, B. Li, M. Gou, and C. Qin, “A New High Capacity Image Steganography Method Combined with Image Elliptic Curve Cryptography and Deep Neural Network,” IEEE Access, vol. 8, 2020, doi: 10.1109/ACCESS.2020.2971528.

T. Indriyani, S. Nurmuslimah, A. Taufiqurrahman, R. K. Hapsari, C. N. Prabiantissa, and A. Rachmad, “Steganography on Color Images Using Least Significant Bit (LSB) Method,” 2023. doi: 10.2991/978-94-6463-174-6_5.

H. W. Tseng and H. S. Leng, “A reversible modified least significant bit (LSB) matching revisited method,” Signal Process. Image Commun., vol. 101, 2022, doi: 10.1016/j.image.2021.116556.

W. F. Al Maki, I. B. Muktyas, S. Arifin, Suwarno, and M. K. B. M. Aziz, “Implementation of a Logistic Map to Calculate the Bits Required for Digital Image Steganography Using the Least Significant Bit (LSB) Method,” J. Comput. Sci., vol. 19, no. 6, 2023, doi: 10.3844/jcssp.2023.686.693.

G. Miftakhul Fahmi, K. N. Isnaini, and D. Suhartono, “IMPLEMENTATION OF STEGANOGRAPHY ON DIGITAL IMAGE WITH MODIFIED VIGENERE CIPHER ALGORITHM AND LEAST SIGNIFICANT BIT (LSB) METHOD,” J. Tek. Inform., vol. 4, no. 2, 2023, doi: 10.52436/1.jutif.2023.4.2.340.

E. H. Rachmawanto, C. A. Sari, Y. P. Astuti, and L. Umaroh, “A robust image watermarking using hybrid DCT and SLT,” in Proceedings - 2016 International Seminar on Application of Technology for Information and Communication, ISEMANTIC 2016, 2017. doi: 10.1109/ISEMANTIC.2016.7873857.

D. Sinaga, E. H. Rachmawanto, C. A. Sari, D. R. I. M. Setiadi, and N. A. Setiyanto, “An Enhancement of Data Hiding Imperceptibility using Slantlet Transform (SLT),” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, 2018, doi: 10.22219/kinetik.v4i1.702.

G. Panda, P. K. Dash, A. K. Pradhan, and S. K. Meher, “Data compression of power quality events using the slantlet transform,” IEEE Trans. Power Deliv., vol. 17, no. 2, 2002, doi: 10.1109/61.997957.

D. R. I. M. Setiadi, “PSNR vs SSIM: imperceptibility quality assessment for image steganography,” Multimed. Tools Appl., vol. 80, no. 6, 2021, doi: 10.1007/s11042-020-10035-z.

P. D. Shah and R. S. Bichkar, “Secret data modification based image steganography technique using genetic algorithm having a flexible chromosome structure,” Eng. Sci. Technol. an Int. J., vol. 24, no. 3, 2021, doi: 10.1016/j.jestch.2020.11.008.

M. Hussain, A. W. A. Wahab, Y. I. Bin Idris, A. T. S. Ho, and K. H. Jung, “Image steganography in spatial domain: A survey,” Signal Process. Image Commun., vol. 65, 2018, doi: 10.1016/j.image.2018.03.012.

X. Xiang and B. Shi, “Evolving generation and fast algorithms of slantlet transform and slantlet-Walsh transform,” Appl. Math. Comput., vol. 269, 2015, doi: 10.1016/j.amc.2015.07.094.

A. Jabbar, A. Hashim, and Q. Al-Doori, “Secured Medical Image Hashing Based on Frequency Domain with Chaotic Map,” Eng. Technol. J., vol. 39, no. 5A, 2021, doi: 10.30684/etj.v39i5a.1786.

L. Widyawati, I. Riadi, and Y. Prayudi, “Comparative Analysis of Image Steganography using SLT, DCT and SLT-DCT Algorithm,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 20, no. 1, 2020, doi: 10.30812/matrik.v20i1.701.

Downloads

Published

2024-12-17

Issue

Section

Articles