Liang, J.; Song, Z.; Sun, Z.; Lv, M.; Ma, H. Coupling Quantum Random Walks with Long- and Short-Term Memory for High Pixel Image Encryption Schemes. Entropy2023, 25, 353.
Liang, J.; Song, Z.; Sun, Z.; Lv, M.; Ma, H. Coupling Quantum Random Walks with Long- and Short-Term Memory for High Pixel Image Encryption Schemes. Entropy 2023, 25, 353.
Liang, J.; Song, Z.; Sun, Z.; Lv, M.; Ma, H. Coupling Quantum Random Walks with Long- and Short-Term Memory for High Pixel Image Encryption Schemes. Entropy2023, 25, 353.
Liang, J.; Song, Z.; Sun, Z.; Lv, M.; Ma, H. Coupling Quantum Random Walks with Long- and Short-Term Memory for High Pixel Image Encryption Schemes. Entropy 2023, 25, 353.
Abstract
This paper proposes an encryption scheme for high pixel density images. Based on the application of the quantum random walk algorithm, the Long short-term memory (LSTM) can effectively solve the problem of low efficiency of the quantum random walk algorithm in generating large-scale pseudorandom matrices, and further improve the statistical properties of the pseudorandom matrices required for encryption. The LSTM is then divided into columns and fed into the LSTM in order for training. Due to the randomness of the input matrix, the LSTM cannot be trained effectively, so the output matrix is predicted to be highly random. The LSTM prediction matrix of the same size as the key matrix is generated based on the pixel density of the image to be encrypted, which can effectively complete the encryption of the image. In the statistical performance test, the proposed encryption scheme achieves an average information entropy of 7.9992, an average number of pixels changed rate (NPCR) of 99.6231%, an average uniform average change intensity (UACI) of 33.6029% and an average correlation of 0.0032. Finally, various noise simulation tests are also conducted to verify its robustness in real-world applications where common noise and attack interference are encountered.
Keywords
image encryption; high pixel density; neural networks; quantum random walk
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
