2,274 research outputs found
Prediction-error of Prediction Error (PPE)-based Reversible Data Hiding
This paper presents a novel reversible data hiding (RDH) algorithm for
gray-scaled images, in which the prediction-error of prediction error (PPE) of
a pixel is used to carry the secret data. In the proposed method, the pixels to
be embedded are firstly predicted with their neighboring pixels to obtain the
corresponding prediction errors (PEs). Then, by exploiting the PEs of the
neighboring pixels, the prediction of the PEs of the pixels can be determined.
And, a sorting technique based on the local complexity of a pixel is used to
collect the PPEs to generate an ordered PPE sequence so that, smaller PPEs will
be processed first for data embedding. By reversibly shifting the PPE histogram
(PPEH) with optimized parameters, the pixels corresponding to the altered PPEH
bins can be finally modified to carry the secret data. Experimental results
have implied that the proposed method can benefit from the prediction procedure
of the PEs, sorting technique as well as parameters selection, and therefore
outperform some state-of-the-art works in terms of payload-distortion
performance when applied to different images.Comment: There has no technical difference to previous versions, but rather
some minor word corrections. A 2-page summary of this paper was accepted by
ACM IH&MMSec'16 "Ongoing work session". My homepage: hzwu.github.i
On the Linear Convergence of the ADMM in Decentralized Consensus Optimization
In decentralized consensus optimization, a connected network of agents
collaboratively minimize the sum of their local objective functions over a
common decision variable, where their information exchange is restricted
between the neighbors. To this end, one can first obtain a problem
reformulation and then apply the alternating direction method of multipliers
(ADMM). The method applies iterative computation at the individual agents and
information exchange between the neighbors. This approach has been observed to
converge quickly and deemed powerful. This paper establishes its linear
convergence rate for decentralized consensus optimization problem with strongly
convex local objective functions. The theoretical convergence rate is
explicitly given in terms of the network topology, the properties of local
objective functions, and the algorithm parameter. This result is not only a
performance guarantee but also a guideline toward accelerating the ADMM
convergence.Comment: 11 figures, IEEE Transactions on Signal Processing, 201
Backstepping controller design for a class of stochastic nonlinear systems with Markovian switching
A more general class of stochastic nonlinear systems with irreducible homogenous Markovian switching are considered in this paper. As preliminaries, the stability criteria and the existence theorem of strong solutions are first presented by using the inequality of mathematic expectation of a Lyapunov function. The state-feedback controller is designed by regarding Markovian switching as constant such that the closed-loop system has a unique solution, and the equilibrium is asymptotically stable in probability in the large. The output-feedback controller is designed based on a quadratic-plus-quartic-form Lyapunov function such that the closed-loop system has a unique solution with the equilibrium being asymptotically stable in probability in the large in the unbiased case and has a unique bounded-in-probability solution in the biased case
Distinguishing Computer-generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning
Computer-generated graphics (CGs) are images generated by computer software.
The~rapid development of computer graphics technologies has made it easier to
generate photorealistic computer graphics, and these graphics are quite
difficult to distinguish from natural images (NIs) with the naked eye. In this
paper, we propose a method based on sensor pattern noise (SPN) and deep
learning to distinguish CGs from NIs. Before being fed into our convolutional
neural network (CNN)-based model, these images---CGs and NIs---are clipped into
image patches. Furthermore, three high-pass filters (HPFs) are used to remove
low-frequency signals, which represent the image content. These filters are
also used to reveal the residual signal as well as SPN introduced by the
digital camera device. Different from the traditional methods of distinguishing
CGs from NIs, the proposed method utilizes a five-layer CNN to classify the
input image patches. Based on the classification results of the image patches,
we deploy a majority vote scheme to obtain the classification results for the
full-size images. The~experiments have demonstrated that (1) the proposed
method with three HPFs can achieve better results than that with only one HPF
or no HPF and that (2) the proposed method with three HPFs achieves 100\%
accuracy, although the NIs undergo a JPEG compression with a quality factor of
75.Comment: This paper has been published by Sensors. doi:10.3390/s18041296;
Sensors 2018, 18(4), 129
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