Research Article
Encryption Analysis of Different Measurement Matrices Based on Compressed Sensing
@INPROCEEDINGS{10.1007/978-3-030-69066-3_6, author={Mengna Shi and Shiyu Guo and Chao Li and Yanqi Zhou and Erfu Wang}, title={Encryption Analysis of Different Measurement Matrices Based on Compressed Sensing}, proceedings={Artificial Intelligence for Communications and Networks. Second EAI International Conference, AICON 2020, Virtual Event, December 19-20, 2020, Proceedings}, proceedings_a={AICON}, year={2021}, month={7}, keywords={Compressed sensing Measurement matrix Chaotic system Encryption}, doi={10.1007/978-3-030-69066-3_6} }- Mengna Shi
Shiyu Guo
Chao Li
Yanqi Zhou
Erfu Wang
Year: 2021
Encryption Analysis of Different Measurement Matrices Based on Compressed Sensing
AICON
Springer
DOI: 10.1007/978-3-030-69066-3_6
Abstract
The randomness of the traditional measurement matrix in compressed sensing is too strong to be implemented on hardware, and when compressed sensing is used for image encryption, the measurement matrix transmitted as a key will consume time and storage space. Combined with the sensitivity of the chaotic system to the initial value, this paper uses Logistic-Chebyshev chaotic map to obtain random sequences with fewer parameters and construct measurement matrix. To test the measurement performance of the chaotic matrix, compare it with the Gaussian measurement matrix and the Bernoulli measurement matrix in the same compression encryption scheme. Pixel scrambling operation is carried out on the compressed image to complete the final encryption step, and the encrypted image is obtained. The reconstruction algorithm adopts the orthogonal matching tracking method to restore the image. The experimental simulation results show that the chaotic matrix has more advantages than the other two random matrices in image quality, and the encryption and decryption time is shorter.