35090461
Multimedia Tampering Localization Based on the Perturbation in Reverse Processing
A new fragile watermarking scheme, which exploits the perturbation in reverse processing, is proposed to enhance the tampering localizability of multimedia authentication. In verifying data integrity or authenticating signatures, the new method performs the reverse processing of watermark embedding. Typically, it solves an embedding equation or de-filters the distributed version instead of really extracting the watermark. If any tampering happened, the output of the method perturbs because the manipulated data, which can be regarded as the observation error, is drastically enlarged by such processing. The perturbed values indicate the degree of the tampering, and their positions directly draw the shapes of the manipulated areas. Compared with the mostly used block-based fragile watermarking, the new method localizes the tampering almost sample-wise other than block-wise. It also supports the adaptive embedding, which does not evenly scale the watermark, and avoids the vulnerabilities resulting from the block-based approaches.
41230139
Comments on an access control model in semantic grid
In this note, we show that there are some significant design weaknesses in the access control model that proposed to address the convergence and consistence problems in semantic grid by Bu and Xu in a recent paper.
32267370
A Tabu Search Based Method for Minimum Sum of Squares Clustering
In this article, the metaheuristic algorithm, tabu search, is proposed to deal with the clustering problem under the criterion of minimum sum of squares clustering. The presented method integrates four moving operations and mutation operation into tabu search. Its superiority over local search clustering algorithms and another tabu clustering approach is extensively demonstrated for artificial and real life data sets.
12064638
SDD: Secure Distributed Diffusion Protocol for Sensor Networks
A sensor network is a collection of tiny sensor nodes, which consists of sensing, data processing and communicating components. Directed Diffusion is an important data-centric routing protocol of sensor networks. In this paper we present the design of a new secure Directed Diffusion protocol (SDD), which provides a secure extension for the Directed Diffusion protocol. We mainly focus on secure routing and give a simple scheme to securely diffuse data. We have not considered with the in-network aggregation as a goal. In order to support the use of SDD for sensor nodes with extremely limited CPU processing capability, we use an efficient one-way chain and do not use asymmetric cryptographic operations in this protocol. Our security analyses show that SDD is robust against any active attackers or compromised nodes in the network.
39321946
Attacks on the (enhanced) Yang-Shieh authentication
The Yang-Shieh authentication is a time-stamp based password authentication scheme that uses smart cards [1]. In [2,3], various attacks on this scheme are described. However, an enhancement of the scheme is proposed in [3] and enables the scheme to resist these existing attacks. In this paper, we show two new attack that can break the enhanced scheme. We further point out that the fundamental computational assumption of the Yang-Shieh authentication scheme is incorrect.
44251234
Efficient and Proactive Threshold Signcryption
To make the system more secure and robust, threshold schemes are proposed to avoid single point failure. At the same time, there are more and more applications which utilize the two basic blocks encryption and digital signature to secure message delivery (such as SSL, SSH). Combining the three tools organically leads to an interesting security tool termed as threshold signcryption which can be used in distributed systems especially the mobile networks. In this paper, we present an efficient threshold signcryption scheme. The scheme is designed for an asynchronous network model which may better present practical distributed systems, especially Internet or mobile ad hoc networks. In order to resist mobile attacks, we add proactive property to our scheme. To the best of our knowledge, the proposed scheme is the first threshold signcryption scheme which is noninteractive, proactive and provably secure and works on asynchronous network models.
10353520
Finding Self-Similarities in Opportunistic People Networks
Opportunistic network is a type of delay tolerant networks (DTN) where network communication opportunities appear opportunistic. In this study, we investigate opportunistic network scenarios based on public network traces, and our contributions are the following: First, we identify the censorship issue in network traces that usually leads to strongly skewed distribution of the measurements. Based on this knowledge, we then apply the Kaplan-Meier Estimator to calculate the survivorship of network measurements, which is used in designing our proposed censorship removal algorithm (CRA) that is used to recover censored data. Second, we perform a rich set of analysis illustrating that UCSD and Dartmouth network traces show strong self-similarity, and can be modeled as such. Third, we pointed out the importance of these newly revealed characteristics in future development and evaluation of opportunistic networks.
18568401
Capture inter-speaker information with a neural network for speaker identification
Many speaker identification systems are created by model-based approaches, where a statistical model is used to characterize a speaker's voice and no inter-speaker information is used in parameter estimation. It is well known that inter-speaker information is very helpful in discrimination of different speakers. We propose a method for the use of inter-speaker information to improve performance of a model-based speaker identification system. A neural network is employed to capture inter-speaker information from output space of those statistical models. In order to sufficiently utilize inter-speaker information, a rival penalized encoding rule is proposed to design supervised learning pairs for training the neural network. Comparative results in the KING speech corpus show that our method leads to a considerable improvement for a model-based speaker identification system.
16909772
On the use of nearest feature line for speaker identification
As a new pattern classification method, nearest feature line (NFL) provides an effective way to tackle the sort of pattern recognition problems where only limited data are available for training. In this paper, we explore the use of NFL for speaker identification in terms of limited data and examine how the NFL performs in such a vexing problem of various mismatches between training and test. In order to speed up NFL in decision-making, we propose an alternative method for similarity measure. We have applied the improved NFL to speaker identification of different operating modes. Its text-dependent performance is better than the dynamic time warping (DTW) on the Ti46 corpus, while its computational load is much lower than that of DTW. Moreover, we propose an utterance partitioning strategy used in the NFL for better performance. For the text-independent mode, we employ the NFL to be a new similarity measure in vector quantization (VQ), which causes the VQ to perform better on the KING corpus. Some computational issues on the NFL are also discussed in this paper.
94451
Lower Bounds for Maximum Parsimony with Gene Order Data
In this paper, we study lower bound techniques for branch-and-bound algorithms for maximum parsimony, with a focus on gene order data. We give a simple O(n3) time dynamic programming algorithm for computing the maximum circular ordering lower bound, where n is the number of leaves. The well-known gene order phylogeny program, GRAPPA, currently implements two heuristic approximations to this lower bounds. Our experiments show a significant improvement over both these methods in practice. Next, we show that the linear programming-based lower bound of Tang and Moret (Tang and Moret, 2005) can be greatly simplified, allowing us to solve the LP in O*n3) time in the worst case, and in O*(n2.5) time amortized over all binary trees. Finally, we formalize the problem of computing the circular ordering lower bound, when the tree topologies are generated bottom-up, as a Path-Constrained Traveling Salesman Problem, and give a polynomial-time 3-approximation algorithm for it. This is a special case of the more general Precedence-Constrained Travelling Salesman Problem and has not previously been studied, to the best of our knowledge.
15814103
Fast and scalable multi-TCAM classification engine for wide policy table lookup
With the explosive growth of Internet traffic, the next generation switches are designed to provide forwarding speed up to 10 Gbps or above. To meet the challenges of delivering wire-speed deep packet inspection for various QoS requirements that making classification becomes the bottleneck of next generation high-speed switches. Ternary content addressable memory (TCAM) provides high-speed parallel comparison operations and is one of the best candidates to implement next-generation hardware-based packet classifiers. Single-TCAM-based solution with ultra-high density has the advantage of simple architecture, but may not be feasible due to the scalability issue; also it may not be fast enough to fulfill the wide-policy-rule classifications, especial for the IPv6-based packets. To provide scalable and deep packet inspection, this paper proposes the pipeline architecture with multiple TCAMs to obtain wire-speed classification for IPv6 and multimedia applications. An algorithm to solve ambiguous cases among the rules is also proposed. Based on the proposed mechanisms, the packet classification engine capable of handling multiple QoS requirements at ultra-high speed is presented.