Dynamically scattering medium-based security-enhanced optical image encryption using orbital angular momentum and deep learning

• A potential optical image encryption with high security level is proposed and demonstrated. • The high level of security stems from the use of both the order key and the model key . • Optical OAMs are used to carry plaintext information so that encrypted information can be coded and converted into speckles. • A dynamic diffuser is designed for encryption and a CNN model is trained for decryption. Scattering medium-based optical image encryption is a practical strategy for information security, but it is challenging to convert speckles caused by scattering into encrypted information. In addition, the security level of the encryption is an issue of concern. Here we propose and demonstrate a potential optical image encryption with high security, in particular using optical orbital angular momentums (OAMs) to carry plaintext information so that encrypted information can be coded and converted into speckles, besides a dynamic diffuser for encryption and a pre-trained convolutional neural network (CNN) model for decryption. The high level of security stems from the use of both the order key (one-to-one correspondence between the labelled OAMs and the speckles) and the model key (unduplicatable pre-trained CNN model). Proof-of-concept experiments to demonstrate the proposed method have been successfully carried out. The image encryption method can open up new prospects for authentication, anti-counterfeiting and encrypted communication.

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Publication Details

Journal
Results in Physics
Published
2024-12-27
DOI
https://doi.org/10.1016/j.rinp.2024.108100
Citations
6
Primary Topic
Chaos-based Image/Signal Encryption
Type
article
Field-Weighted Citation Impact
1.50
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article

Dynamically scattering medium-based security-enhanced optical image encryption using orbital angular momentum and deep learning

Lei Yu, Junbao Hu, Xutao Mo, Dong Wang et al.
6 citations
Results in Physics
Chaos-based Image/Signal Encryption
1.50
article

Dynamically scattering medium-based security-enhanced optical image encryption using orbital angular momentum and deep learning

Lei Yu, Junbao Hu, Xutao Mo, Dong Wang, Xianshan Huang
article en
6 citations

Abstract

• A potential optical image encryption with high security level is proposed and demonstrated. • The high level of security stems from the use of both the order key and the model key . • Optical OAMs are used to carry plaintext information so that encrypted information can be coded and converted into speckles. • A dynamic diffuser is designed for encryption and a CNN model is trained for decryption. Scattering medium-based optical image encryption is a practical strategy for information security, but it is challenging to convert speckles caused by scattering into encrypted information. In addition, the security level of the encryption is an issue of concern. Here we propose and demonstrate a potential optical image encryption with high security, in particular using optical orbital angular momentums (OAMs) to carry plaintext information so that encrypted information can be coded and converted into speckles, besides a dynamic diffuser for encryption and a pre-trained convolutional neural network (CNN) model for decryption. The high level of security stems from the use of both the order key (one-to-one correspondence between the labelled OAMs and the speckles) and the model key (unduplicatable pre-trained CNN model). Proof-of-concept experiments to demonstrate the proposed method have been successfully carried out. The image encryption method can open up new prospects for authentication, anti-counterfeiting and encrypted communication.

Results in PhysicsVol. 69
Anhui East China Institute of Optoelectronic Technology (China) (CN), Anhui University of Technology (CN)
Openalex Percentile: Top 15%
Chaos-based Image/Signal Encryption
1.50
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Dynamically scattering medium-based security-enhanced optical image encryption using orbital angular momentum and deep learning — Lei Yu, Junbao Hu, et al. · Results in Physics (2024) | TGRS Research Map | TGRS