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.
36995505
Fast and accurate binary halftone image resolution increasing by decision-tree learning
Digital halftone is the technique used to convert gray-scale images into binary ones, simulating gray shades by scattering appropriately black and white pixels. Sometimes, there arises the necessity of increasing the resolution of a halftone image. Some recent works have proposed a number of learning-based techniques to zoom binary images. However, they cannot consider a large neighborhood to decide the colors of the resolution-increased pixels, because their running time skyrockets with the growth of the window and the sample images sizes. The use of large window and samples is required to zoom halftone images accurately. This paper presents a new technique to zoom quickly and precisely images generated by any locally-decided halftone algorithm. It is based on decision-tree learning and it is very fast, even using a large window or large samples. The zoomed images obtained by this technique are incredibly sharp and accurate.
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.
1170029
Toward a secure public-key blockwise fragile authentication watermarking
In this paper, we describe some weaknesses of public-key blockwise fragile authentication watermarkings and the means to make them secure. Wong's (1998) original algorithm is not secure against a mere block cut-and-paste or the well-known birthday attack. To make it secure, some schemes have been proposed to make the signature of each block depend on the contents of its neighboring blocks. We attempt to maximize the change localization resolution using only one dependency per block with a scheme we call hash block chaining version 1 (HBC1). We then show that HBC1, as well as any neighbor-dependent scheme, are susceptible to another forgery technique that we have named a transplantation attack. We also show a new kind of birthday attack that can be effectively mounted against HBC1. To thwart these attacks, we propose using a nondeterministic digital signature together with a signature dependent scheme (HBC2). Finally, we discuss the advantages of using discrete logarithm signatures instead of RSA for watermarking.
17092169
Robust anisotropic diffusion to produce enhanced statistical parametric map from noisy fMRI
This paper presents a new, simple, and elegant technique to obtain enhanced statistical parametric maps (SPMs) from noisy functional magnetic resonance imaging (fMRI) data. This technique is based on the robust anisotropic diffusion (RAD), a technique normally used as an edge-preserving filter. A direct application of the RAD to the fMRI data does not work, because in this case RAD would perform an edge-preserving filtering of the fMRI structural information, instead of enhancing its functional information. The RAD can be applied directly to SPM but, in this case, only a small improvement of the SPM quality can be achieved, because the originating fMRI is not taken into account. To overcome these difficulties, we propose to estimate the SPM from the noisy fMRI, compute the diffusion coefficients in the SPM space, and then perform the diffusion in the structural information-removed fMRI data using the coefficients previously computed. These steps are iterated until convergence. We have tested the new technique in both simulated and real fMRI images, yielding surprisingly sharp and noiseless SPMs with increased statistical significance. We also describe how to automatically estimate an appropriate scale parameter.
47477578
DMKB : A Defense Mechanism Knowledge Base
The major cause of the internet incidents is the vulnerability, which is usually exploited by human attackers or worm virus. Thus the implementation of database on vulnerability is most valuable. If vulnerability is found newly, its information can be included in this database. The contemporary vulnerability databases, however, show weakness in describing countermeasures, although they are practically useful in removing or avoiding vulnerability. This comes from the lack of the analysis process on both semantics and patterns for countermeasures. In this paper, we defined the countermeasures in view of defense mechanism and suggested their representation schemes. In semantics, defense mechanisms can be classified into prevention, detection, recovery and tolerance. And they can be expressed in patterns as the composition of aim, condition, and action. Considering these features, we implemented a knowledge base on defense mechanism – DMKB. By using DMKB, users can get almost whole knowledge with keyword searching under GUI. Our DMKB can be utilized in automatic security testing and management for a given system.
4665838
Independent component analysis in a facial local residue space
In this paper, we propose an ICA (Independent Component Analysis) based face recognition algorithm, which is robust to illumination and pose variation. Generally, it is well known that the first few eigenfaces represent illumination variation rather than identity. Most PCA (Principal Component Analysis)-based methods have overcome illumination variation by discarding the projection to a few leading eigenfaces. The space spanned after removing a few leading eigenfaces is called the "residual face space". We found that ICA in the residual face space provides more efficient encoding in terms of redundancy reduction and robustness to pose variation as well as illumination variation, owing to its ability to represent non-Gaussian statistics. Moreover, a face image is separated into several facial components, local spaces, and each local space is represented by the ICA bases (independent components) of its corresponding residual space. The statistical models of face images in local spaces are relatively simple and facilitate classification by a linear encoding. Various experimental results show that the accuracy of face recognition is significantly improved by the proposed method under large illumination and pose variations.
558514
Missing value estimation for DNA microarray gene expression data: local least squares imputation
MOTIVATION
Gene expression data often contain missing expression values. Effective missing value estimation methods are needed since many algorithms for gene expression data analysis require a complete matrix of gene array values. In this paper, imputation methods based on the least squares formulation are proposed to estimate missing values in the gene expression data, which exploit local similarity structures in the data as well as least squares optimization process.


RESULTS
The proposed local least squares imputation method (LLSimpute) represents a target gene that has missing values as a linear combination of similar genes. The similar genes are chosen by k-nearest neighbors or k coherent genes that have large absolute values of Pearson correlation coefficients. Non-parametric missing values estimation method of LLSimpute are designed by introducing an automatic k-value estimator. In our experiments, the proposed LLSimpute method shows competitive results when compared with other imputation methods for missing value estimation on various datasets and percentages of missing values in the data.


AVAILABILITY
The software is available at http://www.cs.umn.edu/~hskim/tools.html


CONTACT
hpark@cs.umn.edu
6362737
Further progress in watermark evaluation testbed (WET)
While Digital Watermarking has received much attention in recent years, it is still a relatively young technology. There are few accepted tools/metrics that can be used to evaluate the suitability of a watermarking technique for a specific application. This lack of a universally adopted set of metrics/methods has motivated us to develop a web-based digital watermark evaluation system called the Watermark Evaluation Testbed or WET. There have been more improvements over the first version of WET. We implemented batch mode with a queue that allows for user submitted jobs. In addition to StirMark 3.1 as an attack module, we added attack modules based on StirMark 4.0. For a new image fidelity measure, we evaluate conditional entropy as an image fidelity measure for different watermarking algorithms and different attacks. Also, we show the results of curve fitting the Receiver Operating Characteristic (ROC) analysis data using the Parzen window density estimation. The curve fits the data closely while having only two parameters to estimate.
16407614
Requirements-based dynamic metrics in object-oriented systems
Because early design decisions can have a major long term impact on the performance of a system, early evaluation of the high-level architecture can be an important risk mitigation technique. This paper proposes a technique for predicting the volume of data that will flow across a network in a distributed system. The prediction is based upon anticipated execution of scenarios and can be applied at an extremely early stage of the design. It is driven by requirements specifications and captures dynamic metrics by defining typical usage patterns in terms of scenarios. Scenarios are then mapped to architectural components, and dataflow across inter-partition links is estimated. The feasibility of the approach is demonstrated through an experiment in which predicted metrics are compared to runtime measurements.