scholarly journals Reversible Privacy Protection with the Capability of Antiforensics

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.

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6336
Author(s):  
Shuai Yang ◽  
Rong Huang ◽  
Fang Han

Image inpainting aims to fill in corrupted regions with visually realistic and semantically plausible contents. In this paper, we propose a progressive image inpainting method, which is based on a forked-then-fused decoder network. A unit called PC-RN, which is the combination of partial convolution and region normalization, serves as the basic component to construct inpainting network. The PC-RN unit can extract useful features from the valid surroundings and can suppress incompleteness-caused interference at the same time. The forked-then-fused decoder network consists of a local reception branch, a long-range attention branch, and a squeeze-and-excitation-based fusing module. Two multi-scale contextual attention modules are deployed into the long-range attention branch for adaptively borrowing features from distant spatial positions. Progressive inpainting strategy allows the attention modules to use the previously filled region to reduce the risk of allocating wrong attention. We conduct extensive experiments on three benchmark databases: Places2, Paris StreetView, and CelebA. Qualitative and quantitative results show that the proposed inpainting model is superior to state-of-the-art works. Moreover, we perform ablation studies to reveal the functionality of each module for the image inpainting task.


Author(s):  
S. Poonguzhali ◽  
Avinash Sharma ◽  
V. Vedanarayanan ◽  
A. Aranganathan ◽  
T. Gomathi ◽  
...  

2021 ◽  
Vol 560 ◽  
pp. 183-201
Author(s):  
Lei Zhang ◽  
Desheng Liu ◽  
Meina Chen ◽  
Hongyan Li ◽  
Chao Wang ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document