2036044
Watermarking JBIG2 text region for image authentication
An authentication watermarking technique (AWT) inserts hidden data into an image in order to detect any accidental or malicious alteration to the image. AWT usually computes, using a secret-key, the message authentication code (MAC) of the image, and somehow inserts MAC into the image itself. The authentication verification is performed using the same secret-key used in the insertion. JBIG2 is an international standard for lossy and lossless binary image compression. It decomposes the image into several regions (text, halftone, line-art) and encodes each one using the most appropriate method. This paper proposes a novel data-hiding technique to embed the data into the text region of a JBIG2 file. We, then, use it to create an AWT for a JBIG2-encoded image. The embedded data can be extracted from either the JBIG2 file itself or the binary image obtained by decoding the JBIG2 file.
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.
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.
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.
8273227
Mapping from BPMN-Formed Business Processes to XPDL Business Processes
In the business process management, many business process execution languages such as XPDL, BPML, BPEL4WS have been specified with different origins and goals. Most of all, XPDL proposed by WfMC has been widely used in the related applications, especially workflows whose concepts are currently interchangeable with those of business processes. On the other hand, Business Process Modeling Notation (BPMN) driven by BPMI has recently been specified as a standardized graphical notation for a business process. We can therefore commonly design and analyze various business processes using the design tools to support BPMN. Notice that a BPMN-formed business process should be converted to its semantically equivalent business process languages such as XPDL which can consequently be executed by business process engines. In this regard, we propose a transformation mechanism from BPMN-formed business processes to corresponding XPDL processes.
4644097
Component-based LDA face description for image retrieval and MPEG-7 standardisation
We propose a method of face description for facial image retrieval from a large data base and for MPEG-7 (Moving Picture Experts Group) standardisation. The novel descriptor is obtained by decomposing a face image into several components and then combining the component features. The decomposition combined with LDA (Linear Discriminant Analysis) provides discriminative facial descriptions that are less sensitive to light and pose changes. Each facial 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. Our algorithm has been developed to deal with the problem of face image retrieval from huge databases such as those found in Internet environments. Such retrieval requires a compact face representation which has robust recognition performance under lighting and pose variations. The partitioning of a face image into components offers a number of benefits that facilitate the development of an efficient and robust face retrieval algorithm. Variation in image statistics due to pose and/or illumination changes within each component region can be simplified and more easily captured by a linear encoding than that of the whole image. So an LDA encoding at the component level facilitates better classification. Furthermore, a facial component can be weighted according to its importance. The component with a large variation is weighted less in the matching stage to yield a more reliable decision. The experimental results obtained on the MPEG-7 data set show an impressive accuracy of our algorithm as compared with other methods including conventional PCA (Principal Component Analysis)/ICA (Independent Component Analysis)/LDA methods and the previous MPEG-7 proposals.
27330844
Efficient processing of regular path joins using PID
Abstract XML is data that has no fixed structure. So it is hard to design a schema for storing and querying an XML data. Instead of a fixed schema, graph-based data models are widely adopted for querying XML. Queries on XML are based on paths in a data graph. A meaningful query usually has several paths in it, but much of recent research is more concerned with optimizing a single path in a query. In this paper, we present an efficient technique for processing multiple path expressions in a query. We implemented our technique and present preliminary performance results.
7309856
Wish branches: combining conditional branching and predication for adaptive predicated execution
Predicated execution has been used to reduce the number of branch mispredictions by eliminating hard-to-predict branches. However, the additional instruction overhead and additional data dependencies due to predicated execution sometimes offset the performance advantage of having fewer mispredictions. We propose a mechanism in which the compiler generates code that can be executed either as predicated code or non-predicated code (i.e., code with normal conditional branches). The hardware decides whether the predicated code or the non-predicated code is executed based on a run-time confidence estimation of the branch's prediction. The code generated by the compiler is the same as predicated code, except the predicated conditional branches are NOT removed - they are left intact in the program code. These conditional branches are called wish branches. The goal of wish branches is to use predicated execution for hard-to-predict dynamic branches and branch prediction for easy-to-predict dynamic branches, thereby obtaining the best of both worlds. We also introduce a class of wish branches, called wish loops, which utilize predication to reduce the misprediction penalty for hard-to-predict backward (loop) branches. We describe the semantics, types, and operation of wish branches along with the software and hardware support required to generate and utilize them. Our results show that wish branches decrease the average execution time of a subset of SPEC INT 2000 benchmarks by 14.2% compared to traditional conditional branches and by 13.3% compared to the best-performing predicated code binary
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.