Tren dan Inovasi dalam Image Hiding untuk Keamanan Informasi Medis: Tinjauan Literatur

Maliatul Fitriyasari

doi:10.31004/riggs.v4i2.854

Authors

Maliatul Fitriyasari Universitas Jember

DOI:

https://doi.org/10.31004/riggs.v4i2.854

Keywords:

image hiding, steganografi, citra medis, keamanan informasi, generative adversial network

Abstract

Pertukaran dan penyimpanan data medis digital, seperti citra hasil MRI, CT-scan, dan X-ray, telah menjadi kebutuhan utama dalam pelayanan kesehatan modern. Namun, meningkatnya volume data medis berbasis jaringan memunculkan ancaman serius terhadap privasi dan keamanan informasi pasien. Salah satu solusi yang banyak dikembangkan adalah image hiding atau steganografi citra, yang memungkinkan penyisipan informasi rahasia ke dalam citra medis tanpa mengurangi kualitas visualnya secara signifikan. Artikel ini menyajikan tinjauan literatur terkini mengenai tren dan inovasi dalam image hiding untuk aplikasi keamanan informasi medis. Studi ini menganalisis berbagai pendekatan steganografi, meliputi teknik berbasis domain spasial dan domain transformasi, serta metode berbasis pembelajaran mesin seperti Generative Adversarial Networks (GAN). Selain itu, tinjauan ini membahas metrik evaluasi yang umum digunakan, seperti Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), dan kapasitas payload. Hasil kajian menunjukkan bahwa integrasi algoritma optimisasi dan teknik deep learning mampu meningkatkan performa steganografi dalam hal kapasitas, kualitas citra stego, dan ketahanan terhadap deteksi. Artikel ini juga merumuskan tantangan penelitian serta arah pengembangan masa depan dalam bidang keamanan informasi medis berbasis image hiding.

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References

S. Gulia, S. Mukherjee, and T. Choudhury, “An extensive literature survey on medical image steganography,” CSI Transactions on ICT, vol. 4, no. 4, pp. 293–298, 2016.

B. A. Shtayt, N. H. Zakaria, and N. H. Harun, “A Comprehensive Review on Medical Image Steganography Based on LSB Technique and Potential Challenges,” Baghdad Science Journal, vol. 18, no. 2(Suppl.), pp. 957–974, 2021.

H. N. AlEisa, “Data Confidentiality in Healthcare Monitoring Systems Based on Image Steganography to Improve the Exchange of Patient Information Using the Internet of Things,” Journal of Healthcare Engineering, vol. 2022, Article ID 7528583, 2022.

A. Singh, R. Kumar, and R. Kaur, “Medical Image Steganography Using LSB and Huffman Encoding,” Procedia Computer Science, vol. 132, pp. 1256–1263, 2018.

M. Elhoseny and A. A. Elghamrawy, “Evaluating the merits and constraints of cryptography-steganography fusion: a systematic analysis,” International Journal of Information Security, vol. 23, no. 1, pp. 1–20, 2024.

J. Liu et al., “Recent Advances of Image Steganography with Generative Adversarial Networks,” arXiv preprint arXiv:1907.01886, 2019.

M. Moher, D. Liberati, J. Tetzlaff, dan D. Altman, "Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement," PLoS Med., vol. 6, no. 7, p. e1000097, 2009.

A. Z. Abadin, R. Sulaiman, dan M. K. Hasan, "Randomization Strategies in Image Steganography Techniques: A Review," Comput. Mater. Contin., vol. 80, no. 2, pp. 3139–3171, 2024.

R. O. Ogundokun, A. O. Ayodele, dan S. O. Fagbohunka, “A Safe and Secured Medical Textual Information Using an Improved LSB Image Steganography,” Int. J. Digit. Multimed. Broadcast., vol. 2021, pp. 1–12, 2021.

A. Rashid, M. Amjad, dan M. Hussain, “Analysis of Steganography Techniques using Least Significant Bit in Grayscale Images and its Extension to Colour Images,” J. Sci. Res. Rep., vol. 8, no. 4, pp. 1–10, 2015.

N. K. Murthy, G. R. Rao, dan B. R. Reddy, “Image Steganography Using Discrete Cosine Transform Algorithm for Medical Images,” dalam Adv. Autom. Signal Process. Instrum. Control, Singapore: Springer, 2021, pp. 219–230.

G. A. Simanungkalit, F. Pasaribu, dan E. L. Sinaga, “Discrete Wavelet Transform (DWT) Based Steganography Implementation,” J. Teknik Indones., vol. 12, no. 2, pp. 123–132, 2022.

X. Zhang, J. Wang, dan Y. Liu, “GAN-Based Steganography for Medical Image Security,” IEEE Access, vol. 9, pp. 34200–34212, 2021.

M. Hasan, M. M. Islam, dan S. A. Al-Mamun, “CNN-Based Image Steganography: A New Approach for Data Hiding in Medical Images,” J. Med. Syst., vol. 44, no. 7, pp. 1–14, 2020.

H. Li, Y., dan Z. Li, “Particle Swarm Optimization Based Data Hiding in Medical Images for Secure Transmission,” IEEE Trans. Inf. Forensics Secur., vol. 15, pp. 1161–1173, 2020.

A. K. Singh dan S. Kumar, “Hybrid Optimization Algorithms for Secure Medical Image Steganography,” Multimed. Tools Appl., vol. 81, no. 6, pp. 8671–8690, 2022.

L., Y. Zhang, dan X. Zhao, “Advances in Machine Learning for Medical Image Security: A Review,” IEEE Rev. Biomed. Eng., vol. 14, pp. 163–176, 2021.

K. A. Zhang, A. Cuesta-Infante, L. Xu, dan K. Veeramachaneni, "SteganoGAN: High Capacity Image Steganography with GANs," arXiv preprint arXiv:1901.03892, 2019.

Y. Luo et al., "A robust medical image steganography approach based on particle swarm optimization and chaotic systems," Soft Comput., vol. 26, pp. 12345–12360, 2022.