As the demand for digital security grows, researchers have developed a new optical system that uses holograms to encode information, creating a level of encryption that traditional methods cannot penetrate. This advance could pave the way for more secure communication channels, helping to protect sensitive data.
“From rapidly evolving digital currencies to governance, health care, communications and social networks, the demand for robust protection systems to combat digital fraud continues to grow,” said research team leader Stelios Tzortzakis from the Institute of Electronic Structure and Laser, Foundation for Research and Technology Hellas and the University of Crete, both in Greece.
“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.”
Writing in Optica, Tzortzakis and colleagues describe the new system, which uses neural networks to retrieve elaborately scrambled information stored as a hologram. They show that trained neural networks can successfully decode the intricate spatial information in the scrambled 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,” said Tzortzakis. “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.”
Scrambling light for security
The researchers developed the new system after discovering that when holograms are used to encode a laser beam, the beam would become completely and randomly scrambled and that the original beam shape could not be recognized or retrieved using physical analysis or calculation. They recognized that this was an ideal way to safely encrypt information.
“The challenge was figuring out how to decrypt the information,” said Tzortzakis. “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.”
To create a physical system that completely and chaotically scrambles light beams, the researchers used a high-power laser interacting with a small cuvette filled with ethanol. The liquid was not only inexpensive but also created the desired chaotic behavior within a short propagation distance of just a few millimeters. In addition to changing the light beam intensity, light that interacted with the liquid also exhibited thermal turbulence that strongly enhanced the chaotic scrambling.
Successful encoding and decoding
To demonstrate the new method, the researchers applied it to encrypt and decode thousands of handwritten digits and other shapes like animals, tools and everyday objects from well-established databases used as references for evaluating image retrieval systems. After optimizing the experimental procedure and training the neural network, they showed that the neural network could accurately retrieve the encoded images 90–95% of the time. They say that this rate could be further improved with more extensive training of the neural network.
The researchers plan to further develop the technology by adding additional levels of protection such as two-factor authentication. Since the biggest hurdle to commercializing the system is the cost and size of the laser system, they are also investigating cost-effective alternatives to expensive, bulky high-power lasers.
More information:
Panagiotis Konstantakis et al, Encrypted Optical Information in Nonlinear Chaotic Systems Uncovered Using Neural Networks, Optica (2024). DOI: 10.1364/OPTICA.530643
Citation:
AI combined with holograms creates an uncrackable optical encryption system (2025, January 30)
retrieved 30 January 2025
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