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Autoencoder-based image encryption using hybrid scrambling, diffusion, and dimensionality reduction
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DOI：10.1016/j.rineng.2026.108977.png)
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Abstract
En 中文
• A novel Computational Auto Encoder model to compress 256 × 256 grayscale images to 128 × 128, reducing data by 4 times. • A novel CNN-based unique vector generating algorithm based on plaintext image properties. • Chaotic map based pseudorandom sequence generation with Ikeda and Henon Maps using generated vectors. The process results in a key that is robust against security attacks. • Comprehensive security analysis demonstrates the resistance against common security attacks, ensuring the algorithm’s reliability and protection. • Reconstruction of decrypted and compressed features results in good visual quality with high PSNR.
Keywords:
Image encryption and decryption
Convolutional neural network (CNN)
Convolutional autoencoder (CAE)
Chaotic maps and peak to signal noise ratio (PSNR)
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