Academic Journal of Computing & Information Science, 2021, 4(3); doi: 10.25236/AJCIS.2021.040301.
Yang Haitao, Changgong Zhang, Jinyu Wang
School of Aerospace Information, University of Aerospace Engineering, 101416, Beijing, China
The reversible information of the remote sensing image of the generative countermeasure network is encrypted and hidden, so as to improve the security performance of the remote sensing image transmission of the generative countermeasure network. A reversible information hiding method for remote sensing images based on block cascade chaotic fitness control is proposed. In a chaotic system, reversible information random coding transmission control protocol construction and random decryption protocol calculation are carried out on reversible information of the remote sensing image of the generative countermeasure network, chaotic mapping decomposition is carried out on the remote sensing image of the generative countermeasure network to be hidden to obtain three gray-scale images, block key matching and random coding are respectively carried out on the three images by using multidimensional Logistics chaotic mapping to generate an image Gaussian random pixel sequence, The features of the coded output generative countermeasure network remote sensing image are recombined by pixel edge scrambling method, and the hidden key is generated under the reversible information random coding transmission control protocol, thus realizing reversible information hiding of the generative countermeasure network remote sensing image. Simulation results show that this method has strong pixel decorrelation, large key space, improved reversible information hiding degree, strong anti-attack ability and good encryption and hiding performance.
generative countermeasure network; Remote sensing image; Reversible information; hide
Yang Haitao, Changgong Zhang, Jinyu Wang. Research on reversible information hiding algorithm of remote sensing image based on generative countermeasure network. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 3: 1-9. https://doi.org/10.25236/AJCIS.2021.040301.
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