|Table of Contents|

Perceptual Hash Algorithm Based on Adaptive PCNNand PCA for Remote Sensing Image Authentication(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2019年02期
Page:
17-22
Research Field:
·数学与计算机科学·
Publishing date:

Info

Title:
Perceptual Hash Algorithm Based on Adaptive PCNNand PCA for Remote Sensing Image Authentication
Author(s):
Ding Kaimeng1; 2; Zhu Changqing3; Luo Wen3; Liu Yueming2
(1.School of Networks and Tele-Communications Engineering,Jinling Institute of Technology,Nanjing 211169,China)(2.State Key Laboratory of Resource and Environment Information System,Institute of Geographic Sciencesand Natural Resources Research,CAS,Beijing 100101,China)(3.Key Laboratory of Virtual Geographic Environment of Ministry of Education,Nanjing Normal University,Nanjing 210023,China)
Keywords:
remote sensing images; perceptual hashing; integrity authentication; adaptive PCNN; PCA
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2019.02.003
Abstract:
Due to the disadvantage of the existing perceptual hash algorithm for remote sensing image has,a perceptual hash algorithm based on adaptive PCNN and PCA for remote sensing image authentication is proposed making use of the characteristic PCNN can suppress noise during edge detection. Firstly,gird division is applied on the remote sensing image,and the parameter of decay time is adaptively defined based on the entropy of the grid. Secondly,the edge feature of each grid is detected by the PCNN,and the feature matrix of the grid is then constructed. Thirdly,the feature matrix is adaptively summarized based on PCA and grid entropy,the result is then encrypted to generate the perceptual hash value of the grid. The experiment results show that the robustness of the algorithm is greatly improved while it is sensitive to malicious tamper of the remote sensing image:it can keep nearly 100% robust to lossless compression and LSB watermark embedding,and can keep more than 95% robust to lossy compression.

References:

[1] LEI J,HAN Z,VAZQUEZ-CASTRO M A,et al. Secure satellite communication systems design with individual secrecy rate constraints[J]. IEEE transactions on information forensics and security,2011,6(3):661-671.
[2]牛夏牧,焦玉华. 感知哈希综述[J]. 电子学报,2008,36(7):1405-1411.
[3]付剑晶,王珂,徐建军. 一种面向多波段数字遥感影像的版权保护方案[J]. 电子学报,2016,44(3):732-739.
[4]丁凯孟,朱颖婷,朱长青,等. 基于Gabor滤波器组与DWT的遥感影像感知哈希认证算法[J]. 铁道学报,2016,38(7):70-76.
[5]WANG K,TANG J,WANG N,et al. Semantic boosting cross-modal hashing for efficient multimedia retrieval[J]. Information sciences,2016,330:199-210.
[6]ZOU F,CHEN Y,SONG J,et al. Compact image fingerprint via multiple Kernel hashing[J]. IEEE transactions on multimedia,2015,17(7):1006-1018.
[7]SUN R,ZENG W. Secure and robust image hashing via compressive sensing[J]. Multimedia tools and applications,2014,70:1651-1665.
[8]OUYANG J,COATRIENX G,SHU H. Robust hashing for image authentication using quaternion discrete Fourier transform and log-polar transform[J]. Digital signal processing,2015,41:98-109.
[9]HAN M,YANG X,JIANGg E. An extreme learning machine based on cellular automata of edge detection for remote sensing images[J]. Neurocomputing,2016,198:27-34.
[10]周立国,冯学智,肖鹏峰,等. 一种频域高分辨率遥感图像线状特征检测方法[J]. 测绘学报,2011,40(3):312-317.
[11]GU J,PAN Y,WANG H. Research on the improvement of image edge detection algorithm based on artificial neural network[J]. Optik-international journal for light and electron optics,2015,126(21):2974-2978.
[12]HELMY A K,EL-TAWEEL G S. Image segmentation scheme based on SOM-PCNN in frequency domain[J]. Applied soft computing,2016,40:405-415.
[13]YANG Z,DONG M,GUO Y,et al. A new method of micro-calcifications detection in digitized mammograms based on improved simplified PCNN[J]. Neurocomputing,2016,218:79-90.
[14]ALA R. A new automatic parameter setting method of a simplified PCNN for image segmentation[J]. IEEE transactions on neural networks,2011,22(6):880-892.
[15]LIU S,HE D,LIANG X. An improved hybrid model for automatic salient region detection[J]. IEEE signal processing letters,2012,19(4):207-210.
[16]邵晓鹏,钟宬,王杨,等. 一种简化PCNN模型在彩色图像边缘检测上的应用[J]. 西安电子科技大学学报(自然科学版),2012,39(6):1-9.
[17]PORTNOY I,MELENDEZ K,PINZON H,et al. An improved weighted recursive PCA algorithm for adaptive fault detection[J]. Control engineering practice,2016,50:69-83.
[18]DELCHAMBRE L. Weighted principal component analysis:a weighted covariance eigendecomposition approach[J]. Monthly notices of the royal astronomical society,2014,446(4):3545-3555.

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Last Update: 2019-06-30