An Enhanced Informed Watermarking Scheme Using the Posterior Hidden Markov Model

Designing a practical watermarking scheme with high robustness, feasible imperceptibility, and large capacity remains one of the most important research topics in robust watermarking. This paper presents a posterior hidden Markov model (HMM-) based informed image watermarking scheme, which well enhances the practicability of the prior-HMM-based informed watermarking with favorable robustness, imperceptibility, and capacity. To make the encoder and decoder use the (nearly) identical posterior HMM, each cover image at the encoder and each received image at the decoder are attacked with JPEG compression at an equivalently small quality factor (QF). The attacked images are then employed to estimate HMM parameter sets for both the encoder and decoder, respectively. Numerical simulations show that a small QF of 5 is an optimum setting for practical use. Based on this posterior HMM, we develop an enhanced posterior-HMM-based informed watermarking scheme. Extensive experimental simulations show that the proposed scheme is comparable to its prior counterpart in which the HMM is estimated with the original image, but it avoids the transmission of the prior HMM from the encoder to the decoder. This thus well enhances the practical application of HMM-based informed watermarking systems. Also, it is demonstrated that the proposed scheme has the robustness comparable to the state-of-the-art with significantly reduced computation time.

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An Informed Watermarking Scheme Using Hidden Markov Model in the Wavelet Domain

IEEE Transactions on Information Forensics and Security ◽

10.1109/tifs.2012.2188797 ◽

2012 ◽

Vol 7 (3) ◽

pp. 853-867 ◽

Cited By ~ 27

Author(s):

Chuntao Wang ◽

Jiangqun Ni ◽

Jiwu Huang

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Wavelet Domain ◽

Watermarking Scheme

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On Hagelbarger’s and Shannon’s matching pennies playing machines

International Journal of Advanced Statistics and IT&C for Economics and Life Sciences ◽

10.2478/ijasitels-2020-0003 ◽

2020 ◽

Vol 10 (1) ◽

pp. 56-66

Author(s):

Macarie Breazu ◽

Daniel Volovici ◽

Daniel I. Morariu ◽

Radu G. Crețulescu

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

State Of The Art ◽

Hidden Markov ◽

Experimental Results ◽

Software Implementation ◽

Mixing Models ◽

Matching Pennies ◽

The Common ◽

Art Research

AbstractIn the 1950s, Hagelbarger’s Sequence Extrapolating Robot (SEER) and Shannon’s Mind-Reading Machine (MRM) were the state-of-the-art research results in playing the well-known “matching pennies” game. In our research we perform a software implementation for both machines in order to test the common statement that MRM, even simpler, beats SEER. Also, we propose a simple contextual predictor (SCP) and use it to compete with SEER and MRM. As expected, experimental results proves the claimed MRM superiority over SEER and even the SCP’s superiority over both SEER and MRM. At the end, we draw some conclusions and propose further research ideas, like the use of mixing models methods and the use of Hidden Markov Model for modelling player’s behaviour.

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Multiple Hidden Markov Model for Pathological Vessel Segmentation

BioMed Research International ◽

10.1155/2018/9868215 ◽

2018 ◽

Vol 2018 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Xin Hu ◽

Deqiong Ding ◽

Dianhui Chu

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Markov Models ◽

State Of The Art ◽

Hidden Markov ◽

Segmentation Method ◽

Section Model ◽

Synthetic Datasets ◽

Overlap Ratio ◽

Vessel Axis

One of the obstacles that prevent the accurate delineation of vessel boundaries is the presence of pathologies, which results in obscure boundaries and vessel-like structures. Targeting this limitation, we present a novel segmentation method based on multiple Hidden Markov Models. This method works with a vessel axis + cross-section model, which constrains the classifier around the vessel. The vessel axis constraint gives our method the potential to be both physiologically accurate and computationally effective. Focusing on pathological vessels, we reap the benefits of the redundant information embedded in multiple vessel-specific features and the good statistical properties coming with Hidden Markov Model, to cover the widest possible spectrum of complex situations. The performance of our method is evaluated on synthetic complex-structured datasets, where we achieve a 91% high overlap ratio. We also validate the proposed method on a real challenging case, segmentation of pathological abdominal arteries. The performance of our method is promising, since our method yields better results than two state-of-the-art methods on both synthetic datasets and real clinical datasets.

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