4807772
Alteration-Locating Authentication Watermarking for Binary Images
In image authentication watermarking, hidden data is inserted into an image to detect any accidental or malicious image alteration. In the literature, quite a small number of cryptography-based secure authentication methods are available for binary images. This paper proposes a new authentication watermarking method for binary images. It can detect any visually significant alteration while maintaining good visual quality for virtually all types of binary images (possibly excluding dispersed-dot halftones). As usual, the security of the algorithm lies only on the secrecy of a secret- or private-key. This paper also presents a variation of the proposed scheme that can locate the modified region with good spatial resolution. A possible application of the proposed technique is in Internet fax transmission, i.e. for legal authentication of documents routed outside the phone network.
13935279
Robust anisotropic diffusion to produce clear statistical parametric map from noisy fMRI
Functional magnetic resonance imaging (fMRI) uses MRI to noninvasively map areas of increased neuronal activity in human brain without the use of an exogenous contrast agent. Low signal-to-noise ratio of fMRI images makes it necessary to use sophisticated image processing techniques, such as statistical parametric map (SPM), to detect activated brain areas. This paper presents a new technique to obtain clear SPM from noisy fMRI data. It is based on the robust anisotropic diffusion. A direct application of the anisotropic diffusion to fMRI does not work, mainly due to the lack of sharp boundaries between activated and non-activated regions. To overcome this difficulty, we propose to calculate SPM from noisy fMRI, compute diffusion coefficients in the SPM space, and then perform the diffusion in fMRI images using the coefficients previously computed. These steps are iterated until the convergence. Experimental results using the new technique yielded surprisingly sharp and noiseless SPMs.
3147895
Pitfalls in public key watermarking
The cryptographic concept of digital signatures is well suited to image watermarking for authentication and integrity verification. However, the peculiar nature of watermarks demands special care in the application of digital signatures for this purpose. We cryptanalyze a recently proposed watermarking scheme and show how it can be strengthened to thwart our attacks, exploring the use of hashing function contexts and signature algorithms based on elliptic curve cryptography.
4790058
A public-key authentication watermarking for binary images
Authentication watermarking is the process that inserts hidden data into an object (image) in order to detect any fraudulent alteration perpetrated by a malicious hacker. In the literature, quite a small number of secure authentication methods are available for binary images. This paper proposes a new secure authentication watermarking method for binary images. It can detect any visually significant alteration while maintaining good visual quality. As usual, the security of the algorithm lies on the secrecy of a private-key. Only its owner can insert the correct watermark while anyone may verify the authenticity through the corresponding public-key. A possible application of the proposed technique is in Internet fax transmission, i.e. for legal authentication of documents routed outside the phone network.
6853667
Efficient Algorithms for Pairing-Based Cryptosystems
We describe fast new algorithms to implement recent cryptosystems based on the Tate pairing. In particular, our techniques improve pairing evaluation speed by a factor of about 55 compared to previously known methods in characteristic 3, and attain performance comparable to that of RSA in larger characteristics. We also propose faster algorithms for scalar multiplication in characteristic 3 and square root extraction over Fpm, the latter technique being also useful in contexts other than that of pairing-based cryptography.
15509328
New features for affine-invariant shape classification
An object seen from different viewpoints results in differently deformed images. Affine-invariant shape classification must classify correctly the object, regardless its viewpoint. In this paper, we propose new local and global features invariant under affine transformation. These features can be used for supervised or unsupervised shape classification, and for shape-based image indexing and retrieval. One of the proposed features is related to the convex deficiency and the others are extracted from the area matrix. Area matrix was used by Shen for the similarity matching in image retrieval. However, differently from the Shen's work, we parameterize the shape contour using the affine-length parameter. This makes our features robust to affine parameterization, while Shen's work does not have this property. Experimental results indicate that our method can classify correctly even highly deformed and noisy shapes using small training sets.
1329357
Data Reduction in Support Vector Machines by a Kernelized Ionic Interaction Model
A major drawback of support vector machines is their high computational complexity. In this paper, we introduce a novel kernelized ionic interaction (IoI) model for data reduction in support vector machines. We also present a data reduction method based on the kernelized instance based (KIB2) algorithm. We show that the computation time can be significantly reduced without any significant decrease in the prediction accuracy.
14176330
System Specification Based Network Modeling for Survivability Testing Simulation
As the structure and behavior of computer network becomes complicated and unpredictable, it becomes difficult to test the survivability of network. The modeling and simulation is a widely used approach to predict the behavior of system or set of system. In this research, we apply a modeling methodology to construct the valid model of computer network focusing on its vulnerability for survivability testing. To accomplish our aim, the appropriate modeling method should be defined. Especially, we take advantage of the system specification based modeling approach to construct valid network model.
18707877
An efficient debugging method for message-based parallel programs using static analysis information
Guaranteeing reproducibility and minimizing probe effects are major issues in the parallel program debugging. The paper suggests a new debugging method, called the Detect and Reproduce method, for message-based parallel programs. The proposed method is designed to guarantee the reproducible behaviors of a target program and to alleviate probe effects by reducing both the number of the probes to be inserted into a program and the amount of information gathered by those probes. The proposed method achieves such effects by employing a static analysis technique to detect nondeterminacy. By detecting nondeterminacy before running the program, only a small number of probes are required to assure reproducibility. To analyze message-based programs statically, an MHB (minimal happened before) model is designed to catch the synchronization structures and algorithms have been developed to locate nondeterminacy based on the MHB model. Utilizing the analysis results, probes are inserted only into the places where nondeterminacy is likely to take place.
8166541
Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis
MOTIVATION
Many practical pattern recognition problems require non-negativity constraints. For example, pixels in digital images and chemical concentrations in bioinformatics are non-negative. Sparse non-negative matrix factorizations (NMFs) are useful when the degree of sparseness in the non-negative basis matrix or the non-negative coefficient matrix in an NMF needs to be controlled in approximating high-dimensional data in a lower dimensional space.


RESULTS
In this article, we introduce a novel formulation of sparse NMF and show how the new formulation leads to a convergent sparse NMF algorithm via alternating non-negativity-constrained least squares. We apply our sparse NMF algorithm to cancer-class discovery and gene expression data analysis and offer biological analysis of the results obtained. Our experimental results illustrate that the proposed sparse NMF algorithm often achieves better clustering performance with shorter computing time compared to other existing NMF algorithms.


AVAILABILITY
The software is available as supplementary material.
15684093
Efficient Authentication and Authorization of Mobile Users Based on Peer-to-Peer Network Mechanisms
This paper presents a peer-to-peer based authentication and authorization infrastructure to minimize authentication delays when mobile users roam across different wireless networks. The basic idea is to avoid exchanging security information between networks visited by a roaming user and the user's home authentication, authorization, and accounting (AAA) server that is typically located in the home network possibly far away from the visited network. Instead, authentication and authorization of a roaming user shall be supported by an AAA server in the visited network. We propose that the AAA server that is responsible for authentication and authorization in a newly visited network locates the AAA server in the previously visited network and retrieves the required security information from that AAA server. The AAA servers can be organized in a peer-to-peer manner and peer-to-peer mechanisms can be applied for searching and transferring security information between them. We propose several mechanisms for quickly locating the previously responsible AAA server in order to decrease authentication delays. The performance of these mechanisms is evaluated by simulations. Real performance measurements show the rather low performance overhead of application level forwarding used in peer-to-peer networks.