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Deep learning decryption approach for asymmetric computer-generated holography (CGH) cryptosystem
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DOI：10.1364/OE.524044.png)
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摘要
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Deep -learning -based optical image decryption has attracted attention due to its remarkable advantages of keyless managements. Here, a high-fidelity deep learning (DL) decryption strategy is proposed, aiming for the asymmetric DRPE-based CGH cryptosystem, which is combined with phase truncation technique and chaotic iris phase masks. First, a mass of ciphertext and plaintext image pairs are generated to create a dataset. Then, a deep neural network, namely ACGHC-Net (network for the asymmetric DRPE-based CGH cryptosystem), is designed and trained in a supervised learning manner. After the model training and tuning, the ACGHCNet can quickly and accurately decrypt the ciphertext images. The average cross -correlation coefficient (CC) of the decrypted images achieves 0.998, the average structural similarity (SSIM) 0.895, and the average peak signal-to-noise ratio (PSNR) 31.090 dB. Furthermore, we conducted anti -noise and anti -clipping analysis on the ACGHC-Net. The results prove that the proposed ACGHC-Net can successfully decrypt the encrypted complex grayscale images, and has good anti -noise and anti -cropping robustness for the asymmetric DRPE-based CGH cryptosystem. The proposed method will be expected to further boost keyless decryption in image encryption systems. (c) 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
Keyword:
OPTICAL-IMAGE ENCRYPTION
DOUBLE-RANDOM-PHASE
SECURITY
期刊
IF:
3.3
论文数:
6.1W
被引数:
14.3W
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