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.
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.
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.
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.
30984122
Fast binary image resolution increasing by k-nearest neighbor learning
In a typical office environment, heterogeneous devices and software, each working in a different spatial resolution, must interact together. Thus, there frequently arises the resolution conversion problem. The present paper addresses the spatial resolution increasing of binary images and documents (for example, conversion of a 300 dpi image into 600 dpi). A new and efficient solution to this problem is proposed. It makes use of k-nearest neighbor learning to automatically design the optimal windowed zoom operator starting from pairs of in-out sample images. The k-nearest neighbor learning has good inductive bias that allows reducing the quantity of the training sample images needed. The resulting operator is stored in a look-up-table, which is extremely fast computationally.
4651323
Face description based on decomposition and combining of a facial space with LDA
We propose a method of efficient face description for facial image retrieval from a large data set. The novel descriptor is obtained by decomposing the face image into several components and then combining the component features. The decomposition combined with LDA (linear discriminant analysis) provides discriminative facial features that are less sensitive to light and pose changes. Each component is represented in its Fisher space and another LDA is then applied to compactly combine the features of the components. To enhance retrieval accuracy further, a simple pose classification and transformation technique is performed, followed by recursive matching. The experimental results obtained on the MPEG-7 data set show an impressive accuracy of our algorithm as compared with the conventional PCA/ICA/LDA methods.
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.
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.
13287397
An analysis of the performance impact of wrong-path memory references on out-of-order and runahead execution processors
High-performance, out-of-order execution 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 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 leads to errors of up to 10 percent (4 percent on average) in IPC estimates for the SPEC CPU2000 integer benchmarks on an out-of-order processor and errors of up to 63 percent on a runahead-execution processor. 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 is 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. We also show that it is crucial to model wrong-path references to accurately estimate the performance improvement provided by runahead execution.
8017153
Predicting how ontologies for the semantic web will evolve
The first phase in the evolution of the semantic Web may be the development of decentralized, adaptive ontologies for software specification.