Spatially Structured-Mode Multiplexing Holography for High-Capacity Security Encryption

Guo, Junjie; Zhang, Yipeng; Hao, Yue; Wang, Luyi; Chen, Pengcheng; Mao, Danjian; Xie, Chenzhu; Chen, Zhenhua; Wu, Xiaofei; Xiao, Min; Zhang, Yong

doi:10.1021/acsphotonics.2c01943

Cited by 18 publications

(7 citation statements)

References 52 publications

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“…Specifically, neurons are connected to other neurons through a secondary wave that is modulated in amplitude or phase by input filed from the earlier layers and the local transmission coefficient. 55 Currently, the DNN has been intensively investigated in image recognition, 56 logic computing, 57 information encryption, 58 and other light applications. 59 Herein, we simulate a DNN to recognize lowcontrast images.…”

Section: Acsmentioning

confidence: 99%

Feature-Enhanced Artificial Visual Perception Based on Superlinear Voltage-Reflectance Responses of Electrochromic Arrays

Yang,

Dong,

Chen

2024

ACS Photonics

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The mimicking of human visual information processing, recognition, and storage is attracting intense interest in the field of artificial intelligence technologies. Electrochromic arrays, directly displaying images that can act as data sets for neuromorphic computing, are advantageous in providing a pathway to energyefficient artificial visual perception. However, improvement of the recognition accuracy of low-contrast images is still a tremendous challenge. To establish a feature-enhancement strategy, a superlinear relationship between the responses and intensities of the input signals needs to be established. In this paper, reflective electrochromic arrays are fabricated by electrodeposition of Prussian blue and a bladed coating of carbon paste. The arrays exhibit a superlinear response of reflectance values at different voltage values. The reflectance almost remains stable in the range from 1.2 to −0.7 V and increases sharply below −0.7 V. The maximum reflectance modulation is as high as 74.8%. To enhance features of low-contrast digital images that are hardly recognized artificially, voltage values are generated proportionally from the grayscales of each pixel of the low-contrast images. Next, the electrochromic arrays display feature-enhanced digital images by controlling the voltage at each pixel. Consequently, artificial neural networks and diffractive neural networks take only 32 and 20 epochs to achieve 100% accuracy in lowcontrast image recognition, respectively. The artificial visual perception design has great potential to realize sensory systems for pattern recognition from complex environments.

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Section: Acsmentioning

confidence: 99%

Feature-Enhanced Artificial Visual Perception Based on Superlinear Voltage-Reflectance Responses of Electrochromic Arrays

Yang,

Dong,

Chen

2024

ACS Photonics

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“…[18] Furthermore, these computational methods have made substantial contributions to optical information security by enhancing system security and increasing the flexibility of optical systems. [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] Among these advancements, computational holography, particularly computer-generated hologram (CGH), has stood out for its remarkable capability to generate nearly arbitrary wavefronts. [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] The simplicity of CGH's reconstruction process through optical diffraction or Fourier transform offers unique advantages in image encryption.…”

Section: Introductionmentioning

confidence: 99%

Computational Imaging Encryption with a Steganographic and Holographic Authentication Strategy

Wang,

Zhou,

Zhang

et al. 2024

Laser & Photonics Reviews

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Robust data security is of paramount importance, particularly for highly classified information. To achieve this, advanced encryption and decryption methods, along with stringent access control and physical security measures, are essential to mitigate the risk of unauthorized access and data exposure. Ghost imaging (GI), a computational imaging technique that operates indirectly, offers a distinct approach compared to traditional spatially resolved imaging techniques. By utilizing computational algorithms, GI enables indirect image reconstruction, making it well‐suited for encryption applications. This proposal introduces a new computational imaging encryption scheme that combines Fourier ghost imaging (FGI) and computational holography. The scheme utilizes an untrained‐tree‐network to generate two camouflaged computer‐generated holograms (CGHs). Integration of CGH‐based steganography and holographic authentication enhances the encoding and decoding processes of FGI encryption. The effectiveness and security of the proposed scheme are rigorously validated through numerical simulations and optical experiments. The results demonstrate promising potential for effectively merging direct and indirect imaging methods in security applications.

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“…As another important property of light, the orbital angular momentum (OAM) modes carried by vortex beams (VBs) are widely used in various fields by virtue of the unique properties brought about by their wavefront structure 51 – 60 In terms of the combination of the normalD2NN with OAM modes, multiplexing/demultiplexing of OAM modes, 61 – 63 optical logic gates, 42 holography, 64 , 65 and spectral detection 66 have been reported in recent years. These works show the great potential of normalD2NN in handling complex OAM modes.…”

Section: Introductionmentioning

confidence: 99%

Advanced all-optical classification using orbital-angular-momentum-encoded diffractive networks

Zhang,

Liao,

Cheng

et al. 2023

Adv. Photon. Nexus

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As a successful case of combining deep learning with photonics, the research on optical machine learning has recently undergone rapid development. Among various optical classification frameworks, diffractive networks have been shown to have unique advantages in all-optical reasoning. As an important property of light, the orbital angular momentum (OAM) of light shows orthogonality and mode-infinity, which can enhance the ability of parallel classification in information processing. However, there have been few all-optical diffractive networks under the OAM mode encoding. Here, we report a strategy of OAM-encoded diffractive deep neural network (OAM-encoded D 2 NN) that encodes the spatial information of objects into the OAM spectrum of the diffracted light to perform all-optical object classification. We demonstrated three different OAM-encoded D 2 NNs to realize (1) single detector OAM-encoded D 2 NN for single task classification, (2) single detector OAM-encoded D 2 NN for multitask classification, and (3) multidetector OAM-encoded D 2 NN for repeatable multitask classification. We provide a feasible way to improve the performance of all-optical object classification and open up promising research directions for D 2 NN by proposing OAMencoded D 2 NN.

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Spatially Structured-Mode Multiplexing Holography for High-Capacity Security Encryption

Cited by 18 publications

References 52 publications

Feature-Enhanced Artificial Visual Perception Based on Superlinear Voltage-Reflectance Responses of Electrochromic Arrays

Feature-Enhanced Artificial Visual Perception Based on Superlinear Voltage-Reflectance Responses of Electrochromic Arrays

Computational Imaging Encryption with a Steganographic and Holographic Authentication Strategy

Advanced all-optical classification using orbital-angular-momentum-encoded diffractive networks

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