4,036 research outputs found
A constructive and unifying framework for zero-bit watermarking
In the watermark detection scenario, also known as zero-bit watermarking, a
watermark, carrying no hidden message, is inserted in content. The watermark
detector checks for the presence of this particular weak signal in content. The
article looks at this problem from a classical detection theory point of view,
but with side information enabled at the embedding side. This means that the
watermark signal is a function of the host content. Our study is twofold. The
first step is to design the best embedding function for a given detection
function, and the best detection function for a given embedding function. This
yields two conditions, which are mixed into one `fundamental' partial
differential equation. It appears that many famous watermarking schemes are
indeed solution to this `fundamental' equation. This study thus gives birth to
a constructive framework unifying solutions, so far perceived as very
different.Comment: submitted to IEEE Trans. on Information Forensics and Securit
Robust Anomaly Detection in Dynamic Networks
We propose two robust methods for anomaly detection in dynamic networks in
which the properties of normal traffic are time-varying. We formulate the
robust anomaly detection problem as a binary composite hypothesis testing
problem and propose two methods: a model-free and a model-based one, leveraging
techniques from the theory of large deviations. Both methods require a family
of Probability Laws (PLs) that represent normal properties of traffic. We
devise a two-step procedure to estimate this family of PLs. We compare the
performance of our robust methods and their vanilla counterparts, which assume
that normal traffic is stationary, on a network with a diurnal normal pattern
and a common anomaly related to data exfiltration. Simulation results show that
our robust methods perform better than their vanilla counterparts in dynamic
networks.Comment: 6 pages. MED conferenc
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
Optimal Asymmetric Binary Quantization for Estimation Under Symmetrically Distributed Noise
Estimation of a location parameter based on noisy and binary quantized
measurements is considered in this letter. We study the behavior of the
Cramer-Rao bound as a function of the quantizer threshold for different
symmetric unimodal noise distributions. We show that, in some cases, the
intuitive choice of threshold position given by the symmetry of the problem,
placing the threshold on the true parameter value, can lead to locally worst
estimation performance.Comment: 4 pages, 5 figure
Distributed Detection and Estimation in Wireless Sensor Networks
In this article we consider the problems of distributed detection and
estimation in wireless sensor networks. In the first part, we provide a general
framework aimed to show how an efficient design of a sensor network requires a
joint organization of in-network processing and communication. Then, we recall
the basic features of consensus algorithm, which is a basic tool to reach
globally optimal decisions through a distributed approach. The main part of the
paper starts addressing the distributed estimation problem. We show first an
entirely decentralized approach, where observations and estimations are
performed without the intervention of a fusion center. Then, we consider the
case where the estimation is performed at a fusion center, showing how to
allocate quantization bits and transmit powers in the links between the nodes
and the fusion center, in order to accommodate the requirement on the maximum
estimation variance, under a constraint on the global transmit power. We extend
the approach to the detection problem. Also in this case, we consider the
distributed approach, where every node can achieve a globally optimal decision,
and the case where the decision is taken at a central node. In the latter case,
we show how to allocate coding bits and transmit power in order to maximize the
detection probability, under constraints on the false alarm rate and the global
transmit power. Then, we generalize consensus algorithms illustrating a
distributed procedure that converges to the projection of the observation
vector onto a signal subspace. We then address the issue of energy consumption
in sensor networks, thus showing how to optimize the network topology in order
to minimize the energy necessary to achieve a global consensus. Finally, we
address the problem of matching the topology of the network to the graph
describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R.
Chellapa and S. Theodoridis, Eds., Elsevier, 201
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