Transferring optical information through random unknown diffusers using a diffractive decoder with electronic encoding

We present a diffractive network (D2NN) design to all-optically perform distinct transformations for different input data classes. This class-specific transformation D2NN processes the input optical field, generating the output optical field whose amplitude or intensity closely approximates the transformed/encrypted version of the input using a transformation matrix specific to the corresponding data class. The original information can be recovered only by applying the class-specific decryption keys to the corresponding class at the diffractive network's output field-of-view. The efficacy of the presented class-specific image encryption framework was validated both numerically and experimentally, tested at 1550 nm and 0.75 mm wavelengths.