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4 October 2024 Learning-based optical cryptosystem based on random speckles
Huanhao Li, Zhipeng Yu, Qi Zhao, Haofan Huang, Puxiang Lai
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Proceedings Volume PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024; PC1311816 (2024) https://doi.org/10.1117/12.3027631
Event: Nanoscience + Engineering, 2024, San Diego, California, United States
Abstract
Multiple scattering enables optical encryption: the input information (i.e., plaintext) is scrambled and only speckles (i.e., cyphertext) are output for access. The decryption, however, is ineffective with conventional methods. Recent intervention of deep learning (DL) decrypts information from speckles with much higher fidelity, driven by massive data. We developed a complex-valued platform to decrypt complex information (e.g., human face) from the speckles, with sufficient-high fidelity for face recognition. Continuous efforts endeavor for higher stability and security. By introducing a spin-multiplexing disordered metasurface as an ultra-stable speckle generator, the system demonstrates excellent decryption efficiency over extended periods in noisy environment with numerous encryption channels and a proposed double-secure scheme provides robust protection for the plaintext with a security key. Also, breaking inherent correlation among the speckles via a speckle modulation network can further boost the security and enable hierarchical authentication encryption. Collectively, speckle-based cryptosystem via DL is promising towards practice.
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Huanhao Li, Zhipeng Yu, Qi Zhao, Haofan Huang, and Puxiang Lai "Learning-based optical cryptosystem based on random speckles", Proc. SPIE PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024, PC1311816 (4 October 2024); https://doi.org/10.1117/12.3027631
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KEYWORDS
Machine learning
Computer security
Network security
Speckle
Education and training
Modulation
Scattering
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