Abstract
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.
Highlights
Because images are vivid, they are widely used by humans and are an important means to express information
We proposed a quantum image encryption method based on DNA Controlled-Not (DNACNot)
Four encryption parameters obtained from quantum image information were used as part of the chaotic initial value
Summary
This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFC0910500, in part by the National Natural Science Foundation of China under Grant 61425002, Grant 61751203, Grant 61772100, Grant 61972266, Grant 61802040, Grant 61672121, and Grant 61572093; in part by the Program for Changjiang Scholars and Innovative Research Team in University under Grant IRT_15R07; in part by the Program for Liaoning Innovative Research Team in University under Grant LT2017012, in part by the Natural Science Foundation of Liaoning Province under Grant 20180551241 and Grant 2019-ZD-0567, in part by the High-Level Talent Innovation Support Program of Dalian City under Grant 2017RQ060 and Grant 2018RQ75, in part by the Dalian Outstanding Young Science and Technology Talent Support Program under Grant 2017RJ08, and in part by the Scientific Research Fund of Liaoning Provincial Education Department under Grant JYT19051.
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