A robust image watermarking in contourlet transform domain

Author(s):  
Rajat Sharma ◽  
Abhishek Kumar Gupta ◽  
Deepak Singh ◽  
Vivek Singh Verma ◽  
Anuj Bhardwaj
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Qiang Wei ◽  
Hu Wang ◽  
Gongxuan Zhang

With the rapid development of Internet and cloud storage, data security sharing and copyright protection are becoming more and more important. In this paper, we introduce a robust image watermarking algorithm for copyright protection based on variational autoencoder networks. The proposed image watermarking embedding and extracting network consists of three parts: encoder subnetwork, decoder subnetwork, and detector subnetwork. In the training process, the encoder and decoder subnetworks learn a robust image representation model and further implement the embedding of 1-bit watermark image to the cover image. Meanwhile, the detector subnetwork learns to extract the 1-bit watermark image from the embedding image. Experimental results demonstrate that the watermarked images generated by the proposed algorithm have better visual effects and are more robust against geometric and noise attacks than traditional approaches in the transform domain.


Author(s):  
OU-JUN LOU ◽  
XIANG-HAI WANG ◽  
ZHENG-XUAN WANG

Coping with geometrical attacks in transform domain is crucial when we design a robust image watermarking scheme. In this paper, a novel contourlet-domain image watermarking scheme, which is robust to common signal processing and geometrical attacks, is proposed. First, the region with maximum energy in the directional subbands is considered for watermarking, i.e. the watermark can be embedded into the significant region as well as highly textured region of the host image. Then, for each coefficient of the selected subband, the strength factor was adaptively adjusted in terms of the energy of its parent and neighbor coefficients. Consequently, the tradeoff between the transparency and robustness of watermark can be achieved. Furthermore, the robust feature points, which can survive various signal processing and affine transformation, are extracted by using the Harris–Laplace detector. In watermark detection, the geometrical distortion of image is identified by using the feature template constructed during embedding phase, i.e. watermark resynchronization is performed. Experimental results show that the proposed watermark scheme is invisible and robust against common signal processing such as median filtering, sharpening, noise adding and JPEG compression, etc and geometrical attacks such as rotation, translation, scaling, row or column removal, shearing and local random bend, etc.


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