A novel joint data-hiding and compression scheme based on SMVQ and image inpainting

Author(s):  
S. Poonguzhali ◽  
Avinash Sharma ◽  
V. Vedanarayanan ◽  
A. Aranganathan ◽  
T. Gomathi ◽  
...  
IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 116027-116037 ◽  
Author(s):  
Xiaolong Liu ◽  
Chia-Chen Lin ◽  
Khan Muhammad ◽  
Fadi Al-Turjman ◽  
Shyan-Ming Yuan

2014 ◽  
Vol 23 (3) ◽  
pp. 969-978 ◽  
Author(s):  
Chuan Qin ◽  
Chin-Chen Chang ◽  
Yi-Ping Chiu

2012 ◽  
Vol 120 (1) ◽  
pp. 59-70 ◽  
Author(s):  
Chuan Qin ◽  
Zhi-Hui Wang ◽  
Chin-Chen Chang ◽  
KuoNan Chen

2016 ◽  
Vol 51 ◽  
pp. 142-155 ◽  
Author(s):  
Chin-Chen Chang ◽  
Thai-Son Nguyen ◽  
Meng-Chieh Lin ◽  
Chia-Chen Lin

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Liyun Dou ◽  
Zichi Wang ◽  
Zhenxing Qian ◽  
Guorui Feng

In this paper, we propose a privacy protection scheme using image dual-inpainting and data hiding. In the proposed scheme, the privacy contents in the original image are concealed, which are reversible that the privacy content can be perfectly recovered. We use an interactive approach to select the areas to be protected, that is, the protection data. To address the disadvantage that single image inpainting is susceptible to forensic localization, we propose a dual-inpainting algorithm to implement the object removal task. The protection data is embedded into the image with object removed using a popular data hiding method. We further use the pattern noise forensic detection and the objective metrics to assess the proposed method. The results on different scenarios show that the proposed scheme can achieve better visual quality and antiforensic capability than the state-of-the-art works.


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