Digital fingerprinting is a technique for identifying users who might try to use multimedia content for unintended purposes, such as redistribution. These fingerprints are typically embedded into the content using watermarking techniques that are designed to be robust to a variety of attacks. A cost-effective attack against such digital fingerprints is collusion, where several differently marked copies of the same content are combined to disrupt the underlying fingerprints. In this paper, we investigate the problem of designing fingerprints that can withstand collusion and allow for the identification of colluders. We begin by introducing the collusion problem for additive embedding. We then study the effect that averaging collusion has upon orthogonal modulation. We introduce an efficient detection algorithm for identifying the fingerprints associated with K colluders that requires O(K log(n/K)) correlations for a group of n users. We next develop a fingerprinting scheme based upon code modulation that does not require as many basis signals as orthogonal modulation. We propose a new class of codes, called anti-collusion codes (ACC), which have the property that the composition of any subset of K or fewer codevectors is unique. Using this property, we can therefore identify groups of K or fewer colluders. We present a construction of binary-valued ACC under the logical AND operation that uses the theory of combinatorial designs and is suitable for both the on-off keying and antipodal form of binary code modulation. In order to accommodate n users, our code construction requires only O(√ n) orthogonal signals for a given number of colluders. We introduce four different detection strategies that can be used with our ACC for identifying a suspect set of colluders. We demonstrate the performance of our ACC for fingerprinting multimedia and identifying colluders through experiments using Gaussian signals and real images.
This paper investigates the protocol design issue for cooperation systems in wireless communications. A tight approximate symbol error rate (SER) for such systems is derived and analyzed. Based on such analysis, an optimum power allocation scheme is proposed by optimizing the derived approximate SER subject to fixed transmission rate and total transmit power constraints. Then, a novel adaptive protocol is proposed for cooperative communications based on minimizing the asymptotic SER (i.e. in an averaging sense under high-enough SNR regimes) of such systems. This proposed adaptive protocol is able to achieve the maximum achievable diversity gain available in such systems without sacrificing any transmission rate or the total transmit power, and optimally adapts the number of cooperation partners under the changing environments. Simulation results show that the proposed adaptive protocol provides a lower SER compared with existing protocols. In addition, the proposed adaptive protocol with optimum power allocation can remarkably enhance the SER performance in comparison with the equal power allocation scheme.
This paper evaluates the performance of the data-bearing approach for pilot-embedding for joint data detection and channel estimation in space-time (ST) coded multiple-input multiple-output (MIMO) systems. Performance measures, such as the minimum mean-squared error (MMSE) of channel estimation, Cramer-Rao lower bound (CRLB), and the Chernoff's bound of the estimated-channel bit error rate (BER) for ST codes, are explored to examine the proposed scheme. The power allocation problem for data and pilot parts is also addressed by optimizing the probability-of-error upper-bound (PEUB) mismatched factor subject to certain constraints. Three kinds of data bearer and pilot structures are investigated via simulations, including time-multiplexing (TM)-based, ST-block-code (STBC)-based, and code-multiplexing (CM)-based data bearer and pilot matrices. Among these three structures, the CM-based scheme provides superior detection performance over the TM-based and the STBC-based schemes for nonquasistatic flat Rayleigh fading channels, while the performances of these three structures are quite close for quasi-static flat Rayleigh fading channels.
We present an approach for the joint segmentation and classification of a time series. The segmentation is on the basis of a menu of possible statistical models: each of these must be describable in terms of a sufficient statistic, but there is no need for these sufficient statistics to be the same, and these can be as complex (for example, cepstral features or autoregressive coefficients) as fits. All that is needed is the probability density function (PDF) of each sufficient statistic under its own assumed model--presumably this comes from training data, and it is particularly appealing that there is no need at all for a joint statistical characterization of all the statistics. There is similarly no need for an a-priori specification of the number of sections, as the approach uses an appropriate penalization of an over-zealous segmentation. The scheme has two stages. In stage one, rough segmentations are implemented sequentially using a piecewise generalized likelihood ratio (GLR); in the second stage, the results from the first stage (both forward and backward) are refined. The computational burden is remarkably small, approximately linear with the length of the time series, and the method is nicely accurate in terms both of discovered number of segments and of segmentation accuracy. A hybrid of the approach with one based on Gibbs sampling is also presented; this combination is somewhat slower but considerably more accurate.
DCE-MRI is a noninvasive functional imaging technique capable of assessing tumor microvasculature clinically. Major limitations associated with conventional region-of-interest (ROI) based compartmental methods include the requirement of invasive acquisition of the input function and labor-intensive identification of the ROIs. We propose a novel blind system identification approach for quantitative imaging by simultaneously estimating the input function and the kinetic parameters. New statistical model is on the pixel domain, whose parameters are initialized using a sub-space based algorithm, and further refined by an iterative maximum likelihood estimation procedure. The performances of the proposed scheme is examined extensively via simulations. The real breast tumor DCE-MRI data are examined by determining the time activity curves and the underlying factor images.
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