Data-Class-Specific All-Optical Transformations and Encryption

Bijie Bai; Heming Wei; Xilin Yang; Tianyi Gan; Deniz Mengu; Mona Jarrahi; Aydogan Ozcan

doi:10.1002/adma.202212091

Data-Class-Specific All-Optical Transformations and Encryption

Adv Mater. 2023 Aug;35(31):e2212091. doi: 10.1002/adma.202212091. Epub 2023 Jun 20.

Authors

Bijie Bai^{1

2}, Heming Wei³, Xilin Yang^{1

2}, Tianyi Gan^{1

4}, Deniz Mengu^{1

2}, Mona Jarrahi^{1

4}, Aydogan Ozcan^{1

2

4}

Affiliations

¹ Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
² Bioengineering Department, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
³ Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai, 200444, China.
⁴ California NanoSystems Institute (CNSI), University of California, Los Angeles, Los Angeles, CA, 90095, USA.

PMID: 37186024
DOI: 10.1002/adma.202212091

Abstract

Diffractive optical networks provide rich opportunities for visual computing tasks. Here, data-class-specific transformations that are all-optically performed between the input and output fields-of-view (FOVs) of a diffractive network are presented. The visual information of the objects is encoded into the amplitude (A), phase (P), or intensity (I) of the optical field at the input, which is all-optically processed by a data-class-specific diffractive network. At the output, an image sensor-array directly measures the transformed patterns, all-optically encrypted using the transformation matrices preassigned to different data classes, i.e., a separate matrix for each data class. The original input images can be recovered by applying the correct decryption key (the inverse transformation) corresponding to the matching data class, while applying any other key will lead to loss of information. All-optical class-specific transformations covering A → A, I → I, and P → I transformations using various image datasets are numerically demonstrated. The feasibility of this framework is also experimentally validated by fabricating class-specific I → I transformation diffractive networks and is successfully tested at different parts of the electromagnetic spectrum, i.e., 1550 nm and 0.75 mm wavelengths. Data-class-specific all-optical transformations provide a fast and energy-efficient method for image and data encryption, enhancing data security and privacy.

Keywords: deep learning; diffractive deep neural networks; diffractive processors; optical computing; two-photon polymerization.

Abstract

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