1. Data‐Class‐Specific All‐Optical Transformations and Encryption
- Author
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Bai, Bijie, Wei, Heming, Yang, Xilin, Gan, Tianyi, Mengu, Deniz, Jarrahi, Mona, and Ozcan, Aydogan
- Subjects
Physical Sciences ,deep learning ,diffractive deep neural networks ,diffractive processors ,optical computing ,two-photon polymerization ,Chemical Sciences ,Engineering ,Nanoscience & Nanotechnology ,Chemical sciences ,Physical sciences - 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.
- Published
- 2023