Robust encryption in diffractive-imaging-based encryption scheme using deep learning

• Autorzy: Ma Shibang , Qin Yi , Gong Qiong , Wang Hongjuan

Identyfikatory

DOI

10.37190/oa240309

• Języki publikacji: EN

Abstrakty

EN

Noise attack is a potential threat to optical cryptosystems because the contaminated ciphertext always yields degraded decrypted result. What is more, such contamination can hardly be eliminated by traditional methods, as the ciphertext itself is also a noise-like image. In this paper, we propose a deep-learning-based approach to deal with this problem. The contaminated ciphertexts, which produce unrecognized decrypted images, can yield high quality ones after being repaired by a deep neural network. We take the diffractive-imaging-based encryption (DIBE) scheme as an example to illustrate our method. Numerical results are presented to show the feasibility and validity of the proposal.

Słowa kluczowe

EN

robust encryption; noise attack deep learning; diffractive-imaging-based encryption

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Optica Applicata
• Rocznik: 2024
• Tom: Vol. 54, nr 3
• Strony: 395--407
• Opis fizyczny: Bibliogr. 21 poz., rys.

Twórcy

autor

Ma Shibang

• College of Mechanical and Electrical Engineering, Nanyang Normal University, Nanyang 473061, China

autor

Qin Yi

• College of Mechanical and Electrical Engineering, Nanyang Normal University, Nanyang 473061, China

autor

Gong Qiong

• College of Mechanical and Electrical Engineering, Nanyang Normal University, Nanyang 473061, China

autor

Wang Hongjuan

• College of Mechanical and Electrical Engineering, Nanyang Normal University, Nanyang 473061, China

Bibliografia

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