A Coverless Image Information Hiding Algorithm Based on Fractal Theory

2020 ◽  
Vol 30 (04) ◽  
pp. 2050062
Author(s):  
Xiang Zhang ◽  
Fei Peng ◽  
Zixing Lin ◽  
Min Long

To improve the robustness and imperceptibility of the existing coverless image information hiding, a generative coverless image information hiding algorithm based on fractal theory is proposed in this paper. Firstly, four fractal image generation methods are analyzed, and the relationship between the coverless information hiding and these methods is discussed. Secondly, based on the fractal image generation algorithm, secret information is hidden by controlling pixel rendering during the generation process. The robustness, imperceptibility, and capability of resisting steganalysis are balanced by adjusting the rendering distance. As it directly generates stego images, this can resist the detection of most existing steganalysis methods. Meanwhile, different capacities can be achieved by adjusting the size of the generated image. Experimental results and analysis show that the proposed scheme can effectively resist steganalysis and has good robustness against various image attacks. Furthermore, it can achieve large capacity, and it has broad prospects for covert communication.

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Yi Cao ◽  
Zhili Zhou ◽  
Q. M. Jonathan Wu ◽  
Chengsheng Yuan ◽  
Xingming Sun

2021 ◽  
Vol 553 ◽  
pp. 19-30
Author(s):  
Qi Li ◽  
Xingyuan Wang ◽  
Xiaoyu Wang ◽  
Bin Ma ◽  
Chunpeng Wang ◽  
...  

2018 ◽  
Vol 35 (sup1) ◽  
pp. 23-33 ◽  
Author(s):  
Jianbin Wu ◽  
Yiwen Liu ◽  
Zhenwei Dai ◽  
Ziyang Kang ◽  
Saman Rahbar ◽  
...  

2021 ◽  
Vol 29 (3) ◽  
pp. 899-914
Author(s):  
Lin Xiang ◽  
Jiaohua Qin ◽  
Xuyu Xiang ◽  
Yun Tan ◽  
Neal N. Xiong

1993 ◽  
Vol 17 (6) ◽  
pp. 705-711 ◽  
Author(s):  
Young Bong Kim ◽  
Hoi Sub Kim ◽  
Hong Oh Kim ◽  
Sung Yong Shin

2018 ◽  
Vol 11 (4) ◽  
pp. 2041-2049 ◽  
Author(s):  
Soumyabrata Dev ◽  
Florian M. Savoy ◽  
Yee Hui Lee ◽  
Stefan Winkler

Abstract. Sky–cloud images obtained from ground-based sky cameras are usually captured using a fisheye lens with a wide field of view. However, the sky exhibits a large dynamic range in terms of luminance, more than a conventional camera can capture. It is thus difficult to capture the details of an entire scene with a regular camera in a single shot. In most cases, the circumsolar region is overexposed, and the regions near the horizon are underexposed. This renders cloud segmentation for such images difficult. In this paper, we propose HDRCloudSeg – an effective method for cloud segmentation using high-dynamic-range (HDR) imaging based on multi-exposure fusion. We describe the HDR image generation process and release a new database to the community for benchmarking. Our proposed approach is the first using HDR radiance maps for cloud segmentation and achieves very good results.


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