Data‐Class‐Specific All‐Optical Transformations and Encryption

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.

Inkjet Printing of Biocompatible Luminescent Organic Crystals for Optical Encryption

“…2-enoic acids (3) have been synthesized by the Claisen condensation method via the reaction of diethyl oxalate (2) with substituted methyl ketones (1) in the presence of sodium methoxide (2 equiv) in methanol, with a subsequent decomposition of the intermediate salt (Scheme 1, condition a). Then, the substituted 2-aminothiophene-3-carboxylic acids (6) have been prepared by Gewald's reaction of the corresponding substituted ketones (4) with ethyl 2-cyanoacetate (5) and sulfur in the one-pot method (Scheme 1, condition b). In the final step, the substituted 2-hydroxy-4oxobut-2-enoic acids (3) react with substituted 2-aminothiophene-3-carboxylic acids ( 6) in ethanol at 60 °C, resulting in the compounds (7a−7f) based on substituted 2-amino-4oxobut-2-enoic acid (Scheme 1).…”

“…The rapid growth of the consumer market raises a problem for protecting personal information and goods from counterfeiting, thus forming a new direction in material science and digital technology to encrypt the information. − One of the promising solutions, combining new materials and encryption technologies, is optical information encryption, since it is a quite fast, remote, safe (including for human health), and low-energy-consumptive approach. − In most cases, optically active or photoluminescent inorganic, organic, and hybrid materials − are utilized for optical encryption through a change in their color or photoluminescent (PL) signal. However, the design and fabrication of the optical security marks on arbitrary surfaces with PL decoding, allowing also a human contact, is still in its infancy.…”

Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network

“…This issue can be partially mitigated by modeling these imperfections and incorporating them into our physical forward model in the form of random variations, which is referred to as the "vaccination" of diffractive networks; [55] vaccinated diffractive optical networks are in general more resilient against fabrication imperfections and exhibit a significantly better match between their numerical and experimental results. [55,58,60,61,63,67,68] In our diffractive multispectral QPI designs, each voxel in the desired multispectral data cube maps to a pixel in the output signal region (S) of the 2D monochrome sensor array; therefore, the total pixel count within S will be equal to the product of the number of wavelength channels (N w ) and the number of pixels per channel (N o ). The total pixel count of an image sensor array or focal plane array can be limited and hard to increase, such as in the case of infrared and terahertz parts of the spectrum.…”

“…These diffractive multispectral QPI processors maintain their performance and phase accuracy despite variations in the intensity of the broadband light sources used for illumination. With the selection of appropriate nano-/ microfabrication methods, such as two-photon polymerizationbased 3D printing, [68,71,72] these diffractive optical processors can be physically scaled (expanded/shrunk) to function within different parts of the electromagnetic spectrum.…”

Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks

“…[1][2][3][4][5][6][7][8][9] These advances in data-driven learning methods have also benefited optical hardware engineering, giving rise to new computing architectures such as diffractive deep neural networks (D 2 NN), which exploit the passive interaction of light with spatially engineered surfaces to perform visual information processing. D 2 NNs, also referred to as diffractive optical networks, diffractive networks, or diffractive processors, have emerged as powerful all-optical processors 9,10 capable of completing various visual computing tasks at the speed of light propagation through thin passive optical devices; examples of such tasks include image classification, [11][12][13] information encryption, [14][15][16][17] and quantitative phase imaging (QPI), 18,19 among others. [20][21][22][23][24] Diffractive optical networks comprise a set of spatially engineered surfaces, the transmission (and/or reflection) profiles of which are optimized using machine-learning techniques.…”