Data Class-Specific Image Encryption Using Optical Diffraction
The Californer/10238992

LOS ANGELES - Californer -- Optical computing has been making strides in recent years, with its potential advantages in speed, energy efficiency, and scalability. Among various photonic devices, diffractive deep neural networks (D²NNs) have gained growing attention as an emerging free-space platform for optical computing. D²NNs, also known as diffractive networks, use deep learning methods to design a series of spatially structured diffractive surfaces that modulate the light diffraction to compute a given task at the speed of light propagation. Furthermore, D²NNs offer unique advantages for visual computing tasks, as they can directly process and access the 2D and 3D spatial information of a scene encoded by the amplitude, phase, polarization, and spectrum of the input light.

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/people/aydogan-ozcan/), demonstrated class-specific diffractive networks that perform the desired transformations for certain input data classes, resulting in optically encrypted images, which can only be recovered using the correct decryption keys.

More on The Californer

• Fibromyalgia, It's Ayurvedic Prevention, Treatment and Cure, Dr. Abhay Kumar Pati, USA
• Home Assault Incident Involving Founder Oliver B. Mitchell III
• Last-Minute Travel Deals for Solo Senior Travelers Include No Single Supplement Opportunities
• Night Guardians Partners with Raven Donuts to Support Animal Rescue and Community Awareness
• Port St. Lucie REALTOR® Irene Pernice Earns SFR® Certification

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) can the encrypted images be restored to reveal the original information, while applying other mismatched inverse transformations result in noise-like patterns. The UCLA team experimentally demonstrated the proof of concept of this class-specific all-optical image encryption at both near-infrared and terahertz wavelengths, validating its feasibility across different parts of the electromagnetic spectrum.

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.

More on The Californer

• AMR and UAS Take the Stage at WorldSkills Shanghai 2026
• City of Long Beach Opens Applications for Third Cohort of Long Beach Fire Corps Program
• Hyperglance Now Accepts Qualifying US Federal Micro-Purchases
• Cozmo AI Launches AI Workforce for Property and Casualty Claims
• Biophysical Therapeutics brings longevity science to skincare with its new IF1 brand

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/10.1002/adma.202212091