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Follow on Google News | Data Class-Specific Image Encryption Using Optical DiffractionBy: UCLA ITA Researchers at UCLA have recently presented a diffractive network to perform data class-specific transformations and optical image encryption. In their paper, published in the journal Advanced Materials, UCLA researchers, led by Professor Aydogan Ozcan (https://samueli.ucla.edu/ In their results, the diffractive networks were trained using deep learning, and then they were physically fabricated using 3D printing to all-optically transform the input images and generate encrypted, uninterpretable output patterns captured by an image sensor. Only by applying the correct decryption keys (i.e., the class-specific inverse transformations) Instead of utilizing a fixed transformation matrix indiscriminately for all classes of input objects, this diffractive network-based image encryption scheme performs a set of pre-determined transformations, each specifically and exclusively assigned to one data class. In contrast, any other input images from undesired data classes will result in noninterpretable, meaningless output images. This class-specific encryption design adds an additional layer of security, and makes it more difficult to decipher the original images that belong to the target data classes by reverse engineering. In addition to enhanced security, this class-specific design enables secure data distribution to multiple end-users, all simultaneously, using only one diffractive encryption network, where different decryption keys can be distributed to different receivers based on their data access permissions. This ensures that only the desired portion of the input data is shared with the authorized users, even though a single diffractive network optically encrypts all these different data classes. The UCLA team acknowledges the support of the US Office of Naval Research. Article: https://doi.org/ End
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