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.

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.

[1] WANG T, YANG J, JI Z, et al. Probabilistic diffusion for interactive image segmentation[J]. IEEE Transactions on Image Processing, 2019, 28(1):330-342.

[2] BAMPIS C G, MARAGOS P, BOVIK A C. Graph-driven diffusion and random walk schemes for image segmentation [J]. IEEE Transactions on Image Processing, 2017:26(1):35-50.

[3] DUAN Youxiang, ZHANG Hanxiao, SUN Qifeng, SUN Youkai. Image super-resolution reconstruction algorithm based on Laplacian pyramid generative adversarial network. Journal of Computer Applications, 2021, 41(4): 1020-1026.

[4] XU Guangxian, XU Shanqiang, GUO Xiaojuan, HUA Yiyang. Image compression-encryption algorithm combined DCT transform with DNA operation[J]. LASER TECHNOLOGY, 2015, 39(6): 806-810.

[5] LIU Hao, SHI Jia-ming, YUAN Zhong-cai, et al. Numerical Simulation and Experimental Study of Multi-band Trace TNT Detection by Imaging. Acta Photonica Sinica, 2016, 45(5): 0514004.

[6] YANG Z M,WANG L Q,WANG Y. Application research of deep learning algorithm in question intention classification[J]. Computer Engineering and Applications, 2019, 55(10):154-160.

[7] HUANG Y, PAISLEY J, LIN Q, et al. Bayesian nonparametric dictionary learning for compressed sensing MRI[J]. IEEE Transactions on Image Processing, 2014, 23(12): 5007-5019. 

[8] SHEN L, SUN G, HUANG Q, et al. Multi-level discriminative dictionary learning with application to large scale image classification[J]. IEEE Transactions on Image Processing, 2015, 24(10): 3109-3123. 

[9] SONG M F,JIA D Z,GUO J W,et al. A point cloud compression algorithm based on K-neighborhood cuboid [J]. Science of Surveying and Mapping, 2019,44(10):93-100.

[10] HU Xueying, GUO Hairu, ZHU Rong. Image super-resolution reconstruction based on hybrid deep convolutional network. Journal of Computer Applications, 2020, 40(7): 2069-2076.

[11] NAN F Z, QIAN Y R,XING Y N,et al. Survey of single image super resolution based on deep learning[J]. Application Research of Computers, 2020,37(2):321-326.

[12] BIAN Y A,LI X,LIU Y,et al. Parallel coordinate descent newton method for efficient L1-regularized loss minimization[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(11):3233-3245.