35762748
Improved SQUARE Attacks against Reduced-Round HIEROCRYPT
We present improved SQUARE attacks against the NESSIE and ECTP candidate block ciphers HIEROCRYPT-3 and HIEROCRYPT-L1, designed by Toshiba. We improve over the previous best known attack on five S-box layers of HIEROCRYPT-3 by a factor of 2 128 computational steps with an attack on six layers for 128-bit keys, and extend it to seven S-box layers for longer keys. For HIEROCRYPT-L1 we are able to improve previous attacks up to seven S-box layers (out of twelve).
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.
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.
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.
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.
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.
9123990
Dimension Reduction in Text Classification with Support Vector Machines
Support vector machines (SVMs) have been recognized as one of the most successful classification methods for many applications including text classification. Even though the learning ability and computational complexity of training in support vector machines may be independent of the dimension of the feature space, reducing computational complexity is an essential issue to efficiently handle a large number of terms in practical applications of text classification. In this paper, we adopt novel dimension reduction methods to reduce the dimension of the document vectors dramatically. We also introduce decision functions for the centroid-based classification algorithm and support vector classifiers to handle the classification problem where a document may belong to multiple classes. Our substantial experimental results show that with several dimension reduction methods that are designed particularly for clustered data, higher efficiency for both training and testing can be achieved without sacrificing prediction accuracy of text classification even when the dimension of the input space is significantly reduced.
5469426
Unraveling condition specific gene transcriptional regulatory networks in Saccharomyces cerevisiae
BackgroundGene expression and transcription factor (TF) binding data have been used to reveal gene transcriptional regulatory networks. Existing knowledge of gene regulation can be presented using gene connectivity networks. However, these composite connectivity networks do not specify the range of biological conditions of the activity of each link in the network.ResultsWe present a novel method that utilizes the expression and binding patterns of the neighboring nodes of each link in existing experimentally-based, literature-derived gene transcriptional regulatory networks and extend them in silico using TF-gene binding motifs and a compendium of large expression data from Saccharomyces cerevisiae. Using this method, we predict several hundreds of new transcriptional regulatory TF-gene links, along with experimental conditions in which known and predicted links become active. This approach unravels new links in the yeast gene transcriptional regulatory network by utilizing the known transcriptional regulatory interactions, and is particularly useful for breaking down the composite transcriptional regulatory network to condition specific networks.ConclusionOur methods can facilitate future binding experiments, as they can considerably help focus on the TFs that must be surveyed to understand gene regulation.(Supplemental material and the latest version of the MATLAB implementation of the United Signature Algorithm is available online at [1] or [see Additional files 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
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.
11226719
Understanding the effects of wrong-path memory references on processor performance
High-performance out-of-order processors spend a significant portion of their execution time on the incorrect program path even though they employ aggressive branch prediction algorithms. Although memory references generated on the wrong path do not change the architectural state of the processor, they can affect the arrangement of data in the memory hierarchy. This paper examines the effects of wrong-path memory references on processor performance. It is shown that these references significantly affect the IPC (Instructions Per Cycle) performance of a processor. Not modeling them can lead to errors of up to 10% in IPC estimates for the SPEC2000 integer benchmarks; 7 out of 12 benchmarks experience an error of greater than 2% in IPC estimates. In general, the error in the IPC increases with increasing memory latency and instruction window size.We find that wrong-path references are usually beneficial for performance, because they prefetch data that will be used by later correct-path references. L2 cache pollution is found to be the most significant negative effect of wrong-path references. Code examples are shown to provide insights into how wrong-path references affect performance.
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.