Abstract
As one promising solution, digital watermarking has been proposed to resolve image copyright protection and content authentication, and has been applied successfully in many fields. Owing to their excellent description capability and invariance property, statistical models have become a popular tool for the image watermarking resulting in favorable trade-offs among imperceptibility, robustness and data payload. By modeling the robust undecimated dual tree complex Wavelet transform (UDTCWT) coefficient magnitudes with the Weibull mixtures based vector hidden Markov trees (HMT) and employing maximum likelihood (ML) test criterion, we propose a new image watermarking approach in UDTCWT domain in this paper. Our image watermarking approach consists of two parts, namely, embedding and extracting. In the embedding process, we compute the robust UDTCWT coefficient magnitudes with UDTCWT domain real/imaginary parts, and insert the digital watermark into the significant UDTCWT coefficient magnitude subband. In the extracting phase, robust UDTCWT coefficient magnitudes are firstly modeled by employing the Weibull mixture-based vector HMT, where the statistical properties of UDTCWT magnitudes are captured accurately. Then the expectation/conditional maximization (ECM) approach is introduced to estimate the statistical model parameters. Finally, an image watermark decoder for multiplicative watermarking is developed using the Weibull mixtures based vector HMT and ML test. The experiments show that the proposed method not only improves the imperceptibility, but also increases the robustness performance and outperforms state-of-the-art methods on a set of standard test images.
Highlights
Digital watermarking has become an active research area focused on digital data protection, such as copyright protection and ownership verification
To overcome the aforementioned critical issues, in this paper, we present a new image watermarking approach in undecimated dual tree complex Wavelet transform (UDTCWT) domain, where the robust UDTCWT coefficient magnitudes are modeled with the Weibull mixtures based vector hidden Markov trees (HMT) and the maximum likelihood (ML) test criterion is utilized
We propose a new HMT statistical model in UDTCWT domain, namely Weibull mixtures-based vector HMT, by describing the UDTCWT magnitudes marginal distribution with Weibull mixture model and grouping UDTCWT magnitudes into a vector, which can capture the heavy-tailed characteristic of UDTCWT magnitudes and the strong inter-scale, and cross-orientation dependencies between them
Summary
Digital watermarking has become an active research area focused on digital data protection, such as copyright protection and ownership verification. P. Niu et al.: Blind Image Watermark Decoder in UDTCWT Domain Using Weibull Mixtures-Based Vector HMT (NSCT) [19], discrete Cosine transform (DCT) [20], and discrete Shearlet transform (DST) [21]. To overcome the aforementioned critical issues, in this paper, we present a new image watermarking approach in UDTCWT domain, where the robust UDTCWT coefficient magnitudes are modeled with the Weibull mixtures based vector HMT and the ML test criterion is utilized. We introduce the optimal ECM algorithm to estimate the statistical model parameters of the Weibull mixture-based vector HMT. We develop a blind UDUCWT domain multiplicative watermark decoder using Weibull mixtures-based vector HMT and ML test criterion, which can decode accurately the hidden message from the host images.
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