15293150
On achievable error exponents for watermarking
Probability-of-error exponents have recently been derived for watermarking systems based on spread-spectrum and quantization-index modulation methods. This paper takes this work one step further and presents minmax error exponents for any embedding scheme and any attack (subject to distortion constraints) at all rates below capacity. The decoders used are universal: they do not know the attck used. Randomized codes outperform deterministic codes, except in the case of memoryless attacks where the same performance is obtained using either kind of code.
39368933
Improved QIM Strategies for Gaussian Watermarking
This paper revisits the problem of watermarking a Gaussian host, where the embedder and attacker are subject to mean-squared distortion constraints. The worst (nonadditive) attack and unconstrained capacity have been identified in previous work. Here we constrain the encoding function to lie in a given family of encoding functions — such as spread-spectrum or fixed-dimensional Quantization Index Modulation (QIM), with or without time-sharing, with or without external dithering. This gives rise to the notion of constrained capacity. Several such families are considered in this paper, and the one that is best under the worst attack is identified for each admissible value of the watermark-to-noise ratio (WNR) and the noise-to-host ratio (NHR). With suitable improvements, even scalar QIM can outperform any (improved) spread-spectrum scheme, for any value of WNR and NHR. The remaining gap to unconstrained capacity can be bridged using higher-dimensional lattice QIM.
15897925
Error exponents for channel coding with side information
Capacity formulas and random-coding and sphere-packing exponents are derived for a generalized family of Gel'fand-Pinsker coding problems. Information is to be reliably transmitted through a noisy channel with random state sequence. Partial information about the state sequence is available to the encoder and decoder. Two families of channels are considered: (1) compound discrete memoryless channels (C-DMC); and (2) channels with arbitrary memory, subject to an additive cost constraint, or more generally to a constraint on the conditional type of the channel output given the input. Both problems are closely connected. For the C-DMC case, our random-coding and sphere-packing exponents coincide at high rates, thereby determining the reliability function of the channel family. The random-coding exponent is achieved using a 3D binning scheme and a maximum penalized mutual information decoder. In the case of arbitrary channels, with memory, a larger random-coding error exponent than in the C-DMC case is obtained. Applications of this study include watermarking, data hiding, communication in presence of partially known interferers, and problems such as broadcast channels, all of which involve the fundamental idea of binning.
7330330
Steganalysis of block-structured stegotext
We study a detection-theoretic approach to steganalysis. The relative entropy between covertext and stegotext determines the steganalyzer's difficulty in discriminating them, which in turn defines the detectability of the stegosystem. We consider the case of Gaussian random covertexts and mean-squared embedding constraint. We derive a lower bound on the relative entropy between covertext and stegotext for block-based embedding functions. This lower bound can be approached arbitrarily closely using a spread-spectrum method and secret keys with large entropy. The lower bound can also be attained using a stochastic quantization index modulation (QIM) encoder, without need for secret keys. In general, perfect undetectability can be achieved for blockwise memoryless Gaussian covertexts. For general Gaussian covertexts with memory, the relative entropy increases approximately linearly with the number of blocks observed by the steganalyzer. The error probabilities of the best steganalysis methods decrease exponentially with the number of blocks.
45767095
A Color Image Segmentation Algorithm by Using Region and Edge Information
A novel segmentation algorithm for natural color image is proposed. Fibonacci Lattice-based Sampling is used to get the symbols of image so as to make each pixel’s label containing color information rather than only as a class marker. Next, Region map is formed based on Fibonacci Lattice symbols to depict homogeneous regions. On the other hand, by applying fuzzy homogeneity algorithm on the image, we filter it to acquire Edge map. To strengthen the ability of discrimination, both the weighted maps are combined to form Region-Edge map. Based on above processes, growing-merging method is used to segment the image. Finally, experiments show very promising results.
5074474
Maximum likelihood computation based on the Fisher scoring and Gauss-Newton quadratic approximations
The Fisher scoring and Gauss-Newton methods are two known methods for maximum likelihood computation. This paper provides a generalization for each method in a unified manner so that they can be used for some difficult maximum likelihood computation, when, for example, there exist constraints on the parameters. A generalized method does not use directly the Newton-type iteration formulas of these methods, but, instead, uses the corresponding quadratic functions transformed from them. It proceeds by repeatedly approximating the log-likelihood function with the quadratic functions in the neighborhoods of the current iterates and optimizing each quadratic function within the parameter space. It is shown that each quadratic function has a weighted linear regression formulation, which can be conveniently solved. This generalization also extends the applicability of the Fisher scoring method to situations when the expected Fisher information matrices are unavailable in closed form. Fast computation can generally be anticipated, owing to their small rates of convergence and a rapid solution of each linear regression problem. While the generalized Gauss-Newton method may sometimes suffer for the so-called large residual problem, the generalized Fisher scoring method has performed consistently well in the numerical experiments we conducted.
16943053
Towards an indoor location system using RF signal strength in IEEE 802.11 networks
Location is one of the most important contexts used in pervasive computing environments. GPS systems are intensely used to detect the location information; they mainly work in outdoor environment. Applications call for precise, easy-to-build, and easy-to-use indoor location systems. This paper presents our work to implement an indoor location determination system for Microsoft-Windows-based platforms using a preexisting IEEE 802.11 wireless network. The location is determined from radio signal strength information collected from multiple base stations at different physical locations. Our experiments show a high accuracy rate of this approach.
14856574
Error Resilient Video Coding Using Unequally Protected Key Pictures
This paper proposes the use of unequally protected key pictures to prevent temporal error propagation in error-prone video communications. The key picture may either be an intra-coded picture or a picture using a long-term motion compensation reference picture through reference picture selection. The inter-coded key picture uses previous key pictures as motion compensation reference. Key pictures are better protected than other pictures using forward error correction in either source or transport coding. Simulation results show significantly improved error resiliency performance of the proposed technique compared to conventional methods.
2472095
Coarse and Reliable Geometric Alignment for Protein Docking
We present an efficient algorithm for generating a small set of coarse alignments between interacting proteins using meaningful features on their surfaces. The proteins are treated as rigid bodies, but the results are more generally useful as the produced configurations can serve as input to local improvement algorithms that allow for protein flexibility. We apply our algorithm to a diverse set of protein complexes from the Protein Data Bank, demonstrating the effectivity of our algorithm, both for bound and for unbound protein docking problems.