Researchers from the Institute of Electronic Structure and Laser, Foundation for Research and Technology Hellas, and the University of Crete have created a new optical system that employs holograms to encode data, establishing a level of encryption that is impenetrable by conventional techniques as the need for digital security increases. This development may protect sensitive data, which may open the door to more secure communication channels. The journal Optica published the study.
Researchers created an optical system that encrypts information as a hologram that is scrambled when sent through a small container of liquid and then uses a neural network for decryption. Image Credit: Stelios Tzortzakis, Institute of Electronic Structure and Laser, Foundation for Research and Technology Hellas
From rapidly evolving digital currencies to governance, healthcare, communications, and social networks, the demand for robust protection systems to combat digital fraud continues to grow. Our new system achieves an exceptional level of encryption by utilizing a neural network to generate the decryption key, which can only be created by the owner of the encryption system.
Stelios Tzortzakis, Research Team Leader, University of Crete
The new system employs neural networks to recover intricately jumbled data stored as a hologram. The researchers demonstrated that trained neural networks can successfully decode the complex spatial information in the jumbled images.
Our study provides a strong foundation for many applications, especially cryptography and secure wireless optical communication, paving the way for next-generation telecommunication technologies. The method we developed is highly reliable even in harsh and unpredictable conditions, addressing real-world challenges like tough weather that often limit the performance of free-space optical systems.
Stelios Tzortzakis, Research Team Leader, University of Crete
Scrambling Light For Security
After learning that a laser beam encoded with holograms would become entirely and randomly jumbled and that the original beam shape could not be identified or recovered through physical analysis or computation, the researchers created the new system. They realized that this was the best method for securely encrypting data.
The challenge was figuring out how to decrypt the information. We came up with the idea of training neural networks to recognize the incredibly fine details of the scrambled light patterns. By creating billions of complex connections, or synapses, within the neural networks, we were able to reconstruct the original light beam shapes. This meant we had a way to create the decryption key that was specific for each encryption system configuration.
Stelios Tzortzakis, Research Team Leader, University of Crete
The researchers used a high-power laser interacting with a small cuvette filled with ethanol to create a physical system that completely and chaotically jumbles light beams. The liquid, which is reasonably priced, produced the intended chaotic behavior at a propagation distance of only a few millimeters.
Light interacting with the liquid also showed thermal turbulence, which significantly increased the chaotic scrambling and altered the intensity of the light beam.
Successful Encoding and Decoding
The researchers used the new technique to encrypt and decode thousands of handwritten numbers and other shapes, such as tools, animals, and common objects, from reputable databases that serve as benchmarks for assessing image retrieval systems.
They demonstrated that the neural network could reliably retrieve the encoded images 90–95% of the time after refining the experimental process and training the network. They claim that this rate could be further increased with more thorough neural network training.
The researchers hope to advance the technology by incorporating extra security features like two-factor authentication. Since the laser system's size and cost are the main obstacles to commercialization, they are also looking into cheaper alternatives to expensive, cumbersome high-power lasers.
Journal Reference:
Konstantakis, P., et al. (2024) Encrypted Optical Information in Nonlinear Chaotic Systems Uncovered Using Neural Networks. Optica. doi.org/10.1364/optica.530643.