NSCT-Based Robust and Perceptual Watermarking for DIBR 3D Images

Abstract

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. INDEX TERMS Depth-image-based rendering (DIBR), digital watermarking, DIBR 3D image watermarking, non-subsampled contourlet transform (NSCT), multi-bit watermarking.

Deep Convolutional Neural Network for Identifying Seam-Carving Forgery

“…In this section, the results of the robustness experiments for the unseen cases that were not considered in the training process of the models are provided. It is important in multimedia forensics to ensure robustness against unconsidered environments, such as digital watermarking [47]- [49], which is robust from various attacks in the distribution process. From this perspective, it is beneficial for a CNN-based forensic approach to be robust against unseen cases.…”

“…Next, we conducted experiments for unseen post-processing of additive white Gaussian noise (AWGN). The AWGN can be applied in the distribution and manipulation of images [47]- [49]; hence, it is important to ensure robustness against AWGN regarding practical forensics. In this experiment, we applied AWGN with a σ value of 0.1 to 0.5 to a mixed test set based on a seam-carving algorithm [3] containing a retargeting ratio of 10% to 50%.…”

Blind Digital Watermarking Framework Based on DTCWT and NSCT for Telemedicine Application

“…The Contourlet Transform (CT) was first presented by Do and Vetteli [26], which is a multi-resolution and multidirectional transform, contours are better exhibited by the Contourlet Transform analogized to divers transforms [27]. Da Cunha et al [28] suggested NSCT, which is the shift invariant model of the CT [29], besides that, in the course of image decomposing and reconstructing, the NSCT abolish down and up samples [30], the Figure 2 shows the NSCT structure. With the aim of reaching multi-scale decomposition, the Non-subsampled Pyramid (NSP), is applied on the image, the result of this operation is low frequencies and high frequencies, then to obtain multi-directional decomposition (see Figure 3), Non-subsampled Directional Filter Bank (NSDFB) is exerted on the high-frequencies for each scale [30].…”

A Blind Watermarking Model of the 3D Object and the Polygonal Mesh Objects for Securing Copyright

“…In modern times, watermarking techniques for a wide range of digital media were utilized as a host cover to hide or embed a piece of information message in such a way that it is imperceptible to a human observer. Usually, the digital media covers can take any form such as images [1][2][3][4][5][6][7], videos [8][9][10][11][12], audio [13][14][15], and DNA sequences [16,17]. Even so the 3D objects are widely available and important, there are a few existing watermarking techniques.…”