Diffractive optical networks provide rich opportunities for visual computing
tasks since the spatial information of a scene can be directly accessed by a
diffractive processor without requiring any digital pre-processing steps. Here
we present data class-specific transformations all-optically performed between
the input and output fields-of-view (FOVs) of a diffractive network. 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 pre-assigned 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. The class-specificity of these
all-optical diffractive transformations creates opportunities where different
keys can be distributed to different users; each user can only decode the
acquired images of only one data class, serving multiple users in an
all-optically encrypted manner. We numerically demonstrated all-optical
class-specific transformations covering A-->A, I-->I, and P-->I transformations
using various image datasets. We also experimentally validated the feasibility
of this framework by fabricating a class-specific I-->I transformation
diffractive network using two-photon polymerization and successfully tested it
at 1550 nm wavelength. Data class-specific all-optical transformations provide
a fast and energy-efficient method for image and data encryption, enhancing
data security and privacy.Comment: 27 Pages, 9 Figures, 1 Tabl