9470216
Quantization index modulation: A class of provably good methods for digital watermarking and information embedding
We consider the problem of embedding one signal (e.g., a digital watermark), within another "host" signal to form a third, "composite" signal. The embedding is designed to achieve efficient tradeoffs among the three conflicting goals of maximizing the information-embedding rate, minimizing the distortion between the host signal and composite signal, and maximizing the robustness of the embedding. We introduce new classes of embedding methods, termed quantization index modulation (QIM) and distortion-compensated QIM (DC-QIM), and develop convenient realizations in the form of what we refer to as dither modulation. Using deterministic models to evaluate digital watermarking methods, we show that QIM is "provably good" against arbitrary bounded and fully informed attacks, which arise in several copyright applications, and in particular it achieves provably better rate distortion-robustness tradeoffs than currently popular spread-spectrum and low-bit(s) modulation methods. Furthermore, we show that for some important classes of probabilistic models, DC-QIM is optimal (capacity-achieving) and regular QIM is near-optimal. These include both additive white Gaussian noise (AWGN) channels, which may be good models for hybrid transmission applications such as digital audio broadcasting, and mean-square-error-constrained attack channels that model private-key watermarking applications.
1524685
Achievable performance of digital watermarking systems
A variety of digital watermarking applications have emerged recently that require the design of systems for embedding one signal (the "embedded signal" or "watermark") within another signal (the "host signal"). We develop a framework for analyzing achievable performance trade-offs of these systems among robustness, distortion, and embedding rate. We also describe a recently introduced class of embedding methods, quantization index modulation (QIM), in which an ensemble of quantizers is constructed and information is embedded by quantizing the host signal with a quantizer associated with the watermark. We introduce an implementation of such a method called spread-transform dither modulation where the embedded information modulates the dither signal of a dithered quantizer, which quantizes projections of the host signal onto a spreading vector. We show that QIM systems have considerable performance advantages over previously proposed spread-spectrum and low-bit modulation systems.
39846081
On-Line Algorithms for Cardinality Constrained Bin Packing Problems
The bin packing problem asks for a packing of a list of items from [0, 1] into the smallest possible number of bins having unit capacity. The k-item bin packing problem additionally imposes the constraint that at most k items are allowed in one bin. We present two efficient approximation algorithms for the on-line version of this problem. We show that, for increasing values of k, the asymptotic worst-case performance ratio of the first algorithm tends towards 2 and that the second algorithm has an asymptotic worst-case performance ratio of 2. Both heuristics considerably improve upon the best known result 2.7 of Krause, Shen and Schwetman. Moreover, we present algorithms for k = 2 and k = 3, where the result for k = 2 is best possible.
10294458
Improvement of authenaticated multiple-key agreement protocol
Recently, Yen and Joye showed that Harn and Lin's authenticated multiple-key agreement protocol is insecure against forgery and consequently proposed a revised protocol to repair it. Later, Wu et al. showed that Yen-Joye revision is also insecure and therefore an improved protocol was proposed. However, Wu et al.'s protocol violates the original requirement in which no one-way hash function is needed. On the other hand, in order to overcome Yen-Joye and Wu et al.'s attacks, Harn and Lin proposed a modified version by modifying the signature signing equation. But the modified version increases one exponentiation in the verification equation. Afterward Zhou et al. showed that the second Harh-Lin protocol is insecure against impersonation attack, i. e., a cheater can impersonate a valid user to execute the protocol successfully and obtains parts of the valid session keys. Then they proposed an improved version to enhance the second Harh-Lin protocol. In this paper, we first show that Zhou et al.'s protocol still suffers the impersonation attack, then we propose an improved scheme that is secure against forgery and doesn't involve any one-way hash function. Compared with all above modified versions, our scheme is secure and efficient.
3424570
Prediction Cubes
In this paper, we introduce a new family of tools for exploratory data analysis, called prediction cubes. As in standard OLAP data cubes, each cell in a prediction cube contains a value that summarizes the data belonging to that cell, and the granularity of cells can be changed via operations such as roll-up and drill-down. In contrast to data cubes, in which each cell value is computed by an aggregate function, e.g., SUM or AVG, each cell value in a prediction cube summarizes a predictive model trained on the data corresponding to that cell, and characterizes its decision behavior or predictiveness. In this paper, we propose and motivate prediction cubes, and show that they can be efficiently computed by exploiting the idea of model decomposition.
10910603
Hybrid Forward Resampling and Volume Rendering
The transforming and rendering of discrete objects, such as traditional images (with or without depths) and volumes, can be considered as resampling problem – objects are reconstructed, transformed, filtered, and finally sampled on the screen grids. In resampling practices, discrete samples (pixels, voxels) can be considered either as infinitesimal sample points (simply called points) or samples of a certain size (splats). Resampling can also be done either forwards or backwards in either the source domain or the target domain. In this paper, we present a framework that features hybrid forward resampling for discrete rendering. Specifically, we apply this framework to enhance volumetric splatting. In this approach, minified voxels are taken simply as points filtered in screen space; while magnified voxels are taken as spherical splats. In addition, we develop two techniques for performing accurate and efficient perspective splatting. The first one is to efficiently compute the 2D elliptical geometry of perspectively projected splats; the second one is to achieve accurate perspective reconstruction filter. The results of our experiments demonstrate both the effectiveness of antialiasing and the efficiency of rendering using this
172784
Algorithms for Structural Comparison and Statistical Analysis of 3D Protein Motifs
The comparison of structural subsites in proteins is increasingly relevant to the prediction of their biological function. To address this problem, we present the Match Augmentation algorithm (MA). Given a structural motif of interest, such as a functional site, MA searches a target protein structure for a match: the set of atoms with the greatest geometric and chemical similarity. MA is extremely efficient because it exploits the fact that the amino acids in a structural motif are not equally important to function. Using motif residues ranked on functional significance via the Evolutionary Trace (ET), MA prioritizes its search by initially forming matches with functionally significant residues, then, guided by ET, it augments this partial match stepwise until the whole motif is found. With this hierarchical strategy, MA runs considerably faster than other methods, and almost always identifies matches in homologs known to have cognate functional sites. Second, in order to interpret matches, we further introduce a statistical method using nonparametric density estimation of the frequency distribution of structural matches. Our results show that the hierarchy of functional importance within structural motifs speeds up the search within targets, and points to a new method to score their statistical significance.