Peningkatan Keamanan Steganografi Citra Berbasis Least Significant Bit dengan Integrasi Algoritma Deep Learning Convolutional Neural Network (CNN)
DOI:
https://doi.org/10.48144/suryainformatika.v15i1.2080Keywords:
CNN, Deep Learning, Steganografi, Keamanan Data, Least Significant Bit (LSB)Abstract
Steganografi citra merupakan teknik penyembunyian informasi rahasia di dalam gambar digital yang berperan penting dalam komunikasi rahasia. Metode Least Significant Bit (LSB) dikenal luas karena kesederhanaan dan kemudahan implementasinya, namun memiliki kelemahan signifikan dalam hal keamanan, khususnya rentan terhadap deteksi melalui analisis statistik. Penelitian ini bertujuan untuk meningkatkan keamanan steganografi LSB melalui integrasi dengan algoritma deep learning Convolutional Neural Network (CNN). Dataset CIFAR-10 digunakan sebagai media eksperimen dengan proses penyisipan data pada bit paling tidak signifikan dari kanal warna citra digital. Evaluasi dilakukan melalui metrik imperseptibilitas seperti Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), serta akurasi deteksi oleh model steganalisis. Hasil eksperimen menunjukkan bahwa integrasi LSB dengan CNN menghasilkan peningkatan nilai PSNR dan SSIM, serta menurunkan tingkat keberhasilan deteksi pesan tersembunyi oleh pihak ketiga. Pendekatan ini berhasil membuat proses penyisipan data lebih adaptif dan sulit dikenali secara visual maupun statistik, sehingga meningkatkan tingkat keamanan dan kerahasiaan dalam komunikasi digital berbasis steganografi.
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