Optical color watermarking based on single-pixel imaging and singular value decomposition in invariant wavelet domain

Qu, Gang; Meng, Xiangfeng; Yang, Xiulun; Wu, Huazheng; Wang, Pengwei; He, Wenqi; Chen, Hongyi

doi:10.1016/j.optlaseng.2020.106376

Cited by 14 publications

(1 citation statement)

References 35 publications

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“…[6] Since the DRPE-based optical encryption has high flexibility and expansion capability, consequently, many other optical encryption methods have also been developed, which proves to be feasible and effective. [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] In recent years, the vulnerability analysis of optical encryption systems has attracted wide interest from researchers. The DRPE-based optical encryption schemes have been verified to be vulnerable to different kinds of attacks [26][27][28][29] due to its inherent properties of linearity and symmetry.…”

Section: Introductionmentioning

confidence: 99%

Deep-learning-based cryptanalysis of two types of nonlinear optical cryptosystems

Wang¹,

Wei²

2022

Chinese Phys. B

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The two types of nonlinear optical cryptosystems (NOCs) that are respectively based on amplitude-phase retrieval algorithm (APRA) and phase retrieval algorithm (PRA) have attracted a lot of attention due to their unique mechanism of encryption process and remarkable ability to resist common attacks. In this paper, for the first time, to the best of our knowledge, the security of the two types NOCs is evaluated by using a deep-learning (DL) method, wherein an end-to-end densely connected convolutional network (DenseNet) model for cryptanalysis is developed. The proposed DL-based method is able to retrieve unknown plaintexts from the given ciphertexts by using the trained DenseNet model without a prior knowledge of any public or private keys. The results of numerical experiments with the DenseNet model clearly demonstrate the validity and good performance of the proposed the DL-based attack on NOCs.

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