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Depth-image-based rendering (DIBR), where arbitrary views are synthesized from a center image and depth image, has received much attention in the three-dimensional (3D) research field. With advances in depth-acquisition techniques and the proliferation of 3D glasses and 3D display devices, there is a growing demand for schemes to protect the copyrights of DIBR 3D images. Digital watermarking is a typical protection technology and designing a watermarking method for DIBR 3D images is a challenging task because the synchronization of watermarks can easily be broken in the process of generating synthetic images. To address this issue, we propose a non-subsampled contourlet transform (NSCT)-based blind watermarking for DIBR 3D images. To ensure the proposed method has properties of robustness against the DIBR process, we conduct an analysis of robustness for NSCT subbands against DIBR attacks. Based on the analysis results, we select subbands that are robust against DIBR attacks and embed watermark in the low coefficients of NSCT subbands using quantization-based embedding. While ensuring robustness, the proposed method also improves the imperceptibility of watermarks by adjusting their embedding strength according to computed perceptual masking values. Through experiments, we show that the proposed method is robust against both desynchronization attacks of the DIBR process and common attacks including signal processing operations and geometric distortions. The high imperceptibility of our method is also verified by several evaluation metrics in a subjective and objective manner.

In this paper, we propose a convolutional neural network-based template architecture that compensates for the disadvantages of existing watermarking techniques that are vulnerable to geometric distortion. The proposed template consists of a template generation network, a template extraction network, and a template matching network. The template generation network generates a template in the form of noise and the template is inserted into certain pre-defined spatial locations of the image. The extraction network detects spatial locations where the template is inserted in the image. Finally, the template matching network estimates the parameters of the geometric distortion by comparing the shape of spatial locations where the template was inserted with the locations where the template was detected. It is possible to recover an image in its original geometrical form using the estimated parameters, and as a result, watermarks applied using existing watermarking techniques that are vulnerable to geometric distortion can be decoded normally.

Several depth image-based rendering (DIBR) watermarking methods have been proposed, but they have various drawbacks, such as non-blindness, low imperceptibility and vulnerability to signal or geometric distortion. This paper proposes a template-based DIBR watermarking method that overcomes the drawbacks of previous methods. The proposed method exploits two properties to resist DIBR attacks: the pixel is only moved horizontally by DIBR, and the smaller block is not distorted by DIBR. The one-dimensional (1D) discrete cosine transform (DCT) and curvelet domains are adopted to utilize these two properties. A template is inserted in the curvelet domain to restore the synchronization error caused by geometric distortion. A watermark is inserted in the 1D DCT domain to insert and detect a message from the DIBR image. Experimental results of the proposed method show high imperceptibility and robustness to various attacks, such as signal and geometric distortions. The proposed method is also robust to DIBR distortion and DIBR configuration adjustment, such as depth image preprocessing and baseline distance adjustment.