PERBANDINGAN KUALITAS CITRA GRAYSCALE STEGANOGRAFI METODE LSB DAN DCT BERDASARKAN PSNR DAN SSIM
DOI:
https://doi.org/10.24076/joism.2026v7i2.2492Keywords:
Steganografi, Least Significant Bit (LSB), Discrete Cosine Transform (DCT), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM)Abstract
Penelitian ini membandingkan kualitas citra grayscale hasil steganografi menggunakan metode Least Significant Bit (LSB) dan Discrete Cosine Transform (DCT). Perbandingan ini penting karena LSB dan DCT mewakili dua pendekatan utama steganografi, yaitu domain spasial dan domain frekuensi, yang memiliki perbedaan karakteristik dalam kapasitas penyisipan dan kualitas visual citra. Penelitian dilakukan secara eksperimental menggunakan dataset USC-SIPI dengan variasi parameter LSB (1, 2, dan 4 bit) serta DCT (strength 0.01 dan 0.05). Kualitas citra hasil penyisipan dievaluasi menggunakan Peak Signal-to-Noise Ratio (PSNR) dan Structural Similarity Index Measure (SSIM). Hasil penelitian menunjukkan bahwa DCT-strength 0.01 menghasilkan kualitas citra terbaik dengan PSNR 57.47 dB dan SSIM 0.9997. Sementara itu, LSB-1bit memberikan keseimbangan terbaik antara kualitas citra dan kapasitas penyisipan. Hasil ini menunjukkan adanya trade-off antara kualitas dan kapasitas pada kedua metode.
References
[1] N. Min-Allah et al., “Quantum Image Steganography Schemes for Data Hiding: A Survey,” Appl. Sci., vol. 12, no. 20, 2022, doi: 10.3390/app122010294.
[2] M. Farhan Syakir and P. Studi Sistem dan Teknologi Informasi, “Analisis Penerapan dan Penentuan Teknik Terbaik untuk Steganographic Watermark Beserta Digial Signature Pada Video,” 2024.
[3] R. Fahmi, N. Imanudin, I. Kustiawan, and S. Elvyanti, “Steganografi Citra Digital Menggunakan Pendekatan Least Significant Bit dan Discrete Cosine Transform,” Semin. Nas. Tek. …, no. 207, pp. 1–5, 2023, [Online]. Available: https://snte.fortei.org/list/index.php/snte/article/view/48%0Ahttps://snte.fortei.org/list/index.php/snte/article/download/48/50
[4] I. U. W. Mulyono, Y. Kusumawati, and N. K. Ningrum, “Analisa Visual Citra Hasil Kombinasi Steganografi dan Kriptografi Berbasis Least Significant Bit Dalam Cipher,” J. Masy. Inform., vol. 14, no. 1, pp. 16–28, 2023, doi: 10.14710/jmasif.14.1.51484.
[5] A. Kumar, P. Singla, and A. Yadav, “StegaVision: Enhancing Steganography with Attention Mechanism,” pp. 1–2, 2024, [Online]. Available: http://arxiv.org/abs/2411.05838
[6] T. Qiao et al., “Robust steganography in practical communication: a comparative study,” Eurasip J. Image Video Process., vol. 2023, no. 1, 2023, doi: 10.1186/s13640-023-00615-y.
[7] R. Apau, M. Asante, F. Twum, J. Ben Hayfron-Acquah, and K. O. Peasah, Image steganography techniques for resisting statistical steganalysis attacks: A systematic literature review, vol. 19, no. 9 September. 2024. doi: 10.1371/journal.pone.0308807.
[8] D. A. N. S. Lsb and L. S. B. Steganography, “Analysis Of Image Effect On The Combination Of RSA Cryptography And LSB Steganography,” J. Teknol. Inf. dan Ilmu Komput., vol. 9, no. 2, pp. 279–286, 2022, doi: 10.25126/jtiik.202294852.
[9] M. Alanzy, R. Alomrani, B. Alqarni, and S. Almutairi, “Image Steganography Using LSB and Hybrid Encryption Algorithms,” Appl. Sci., vol. 13, no. 21, 2023, doi: 10.3390/app132111771.
[10] N. Subramanian, O. Elharrouss, S. Al-Maadeed, and A. Bouridane, “Image Steganography: A Review of the Recent Advances,” IEEE Access, vol. 9, pp. 23409–23423, 2021, doi: 10.1109/ACCESS.2021.3053998.
[11] A. D. A. N. Dct, “Steganografi Metode Inverted Lsb Menggunakan Pola,” vol. 7, no. 2, pp. 218–230, 2024.
[12] M. H. Kombrink, Z. J. M. H. Geradts, and M. Worring, “Image Steganography Approaches and Their Detection Strategies: A Survey,” ACM Comput. Surv., vol. 57, no. 2, 2024, doi: 10.1145/3694965.
[13] D. Zulfikar and H. Hermanto, “Hamming Code in JPEG Image Steganography within the Discrete Cosine Transform Domain,” J. Appl. Informatics Comput., vol. 9, no. 3, pp. 868–875, 2025, doi: 10.30871/jaic.v9i3.9387.
[14] Z. Zhou et al., “Secret-to-Image Reversible Transformation for Generative Steganography,” IEEE Trans. Dependable Secur. Comput., vol. 20, no. 5, pp. 4118–4134, 2023, doi: 10.1109/TDSC.2022.3217661.
[15] W. Rehman, “A Novel Approach to Image Steganography Using Generative Adversarial Networks,” pp. 1–17, 2024, [Online]. Available: http://arxiv.org/abs/2412.00094
[16] V. Himthani, V. S. Dhaka, M. Kaur, G. Rani, M. Oza, and H. N. Lee, “Comparative performance assessment of deep learning based image steganography techniques,” Sci. Rep., vol. 12, no. 1, pp. 1–16, 2022, doi: 10.1038/s41598-022-17362-1.
[17] Y. Al Najjar, “Comparative Analysis of Image Quality Assessment Metrics: MSE, PSNR, SSIM and FSIM,” Int. J. Sci. Res., vol. 13, no. 3, pp. 110–114, 2024, doi: 10.21275/sr24302013533.
[18] K. Ohashi et al., “Applicability Evaluation of Full-Reference Image Quality Assessment Methods for Computed Tomography Images,” J. Digit. Imaging, vol. 36, no. 6, pp. 2623–2634, 2023, doi: 10.1007/s10278-023-00875-0.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Information System Management (JOISM)
This work is licensed under a Creative Commons Attribution 4.0 International License.
