A Quantum Image Encryption Method Based on DNACNot

Shihua Zhou

doi:10.1109/ACCESS.2020.3027964

Return

A Quantum Image Encryption Method Based on DNACNot

2020-01-01

周士华 (Shihua Zhou) *

DOI：10.1109/ACCESS.2020.3027964

Original

Save

View PDF

Abstract

En 中文

The image is one of the most important means to store information, and is widely used in every aspect of life. However, the characteristics of images enable them to be easily stolen, tampered with, and copied. Researchers have proposed many encryption methods, with most applying only to traditional digital images. There are few encryption methods for quantum images, and quantum image-oriented encryption technology has begun to attract researchers' attention. This study proposes a quantum image encryption method based on DNA Controlled-Not (DNACNot). Based on the quantum image information, encryption parameters are obtained and transferred as part of a chaotic initial key. Two natural DNA sequences are amplified to obtain the DNA coordinate sequence, and a modified chaos game representation is used to modify the DNA coordinate sequence, which is then used to correct the sequence generated by chaos. The resulting sequence is converted to an integer sequence to obtain DNACNot. A controlled-not operation is performed between DNACNot and the quantum image is scrambled by bit-plane to obtain the encrypted image. The encryption method has high security, a good encryption effect, and a large key space. The method can effectively resist exhaustive, statistical, and differential attacks. The algorithm is easy to implement at a low cost. The encryption time of our proposed method is satisfactory, and the method is suitable for real-time encryption. Moreover, the encryption results can be transmitted over the internet and stored in the cloud.

Keywords:

Encryption

DNA

Chaos

Games

Manganese

Transforms

Quantum image

encryption

DNA sequence

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.