Forensic analysis of nonlinear collusion attacks for multimedia fingerprinting
Digital fingerprinting is a technology for tracing the distribution of multimedia content and protecting them from unauthorized redistribution. Unique identification information is embedded into each distributed copy of multimedia signal and serves as a digital fingerprint. Collusion attack is a cost-effective attack against digital fingerprinting, where colluders combine several copies with the same content but different fingerprints to remove or attenuate the original fingerprints. In this paper, we investigate the average collusion attack and several basic nonlinear collusions on independent Gaussian fingerprints, and study their effectiveness and the impact on the perceptual quality. With unbounded Gaussian fingerprints, perceivable distortion may exist in the fingerprinted copies as well as the copies after the collusion attacks. In order to remove this perceptual distortion, we introduce bounded Gaussian-like fingerprints and study their performance under collusion attacks. We also study several commonly used detection statistics and analyze their performance under collusion attacks. We further propose a preprocessing technique of the extracted fingerprints specifically for collusion scenarios to improve the detection performance.

title: Dynamic join-exit amortization and scheduling for time-efficient group key agreement
abstract: We propose a time-efficient contributory key agreement framework for secure communications in dynamic groups. The proposed scheme employs a special join-tree/exit-tree topology in the logical key tree and effectively exploits the efficiency of amortized operations. We derive the optimal parameters and design an activation algorithm for the join and exit trees. We also show that the asymptotic average time cost per user join and leave event is /spl theta/(log (log n)), where n is the group size. Our experiment results on both simulated user activities and the real MBone data have shown that the proposed scheme outperforms the existing tree-based schemes.

title: Watermarking for image authentication
abstract: A data embedding method is proposed for image authentication based on table look-up in frequency domain. A visually meaningful watermark and a set of simple features are embedded invisibly in the marked image, which can be stored in the compressed form. The scheme can detect and localize alterations of the original image, such as the tampering of images exported from a digital camera.

title: Data hiding in binary image for authentication and annotation
abstract: This paper proposes a new method to embed data in binary images, including scanned text, figures, and signatures. The method manipulates "flippable" pixels to enforce specific block-based relationship in order to embed a significant amount of data without causing noticeable artifacts. Shuffling is applied before embedding to equalize the uneven embedding capacity from region to region. The hidden data can be extracted without using the original image, and can also be accurately extracted after high quality printing and scanning with the help of a few registration marks. The proposed data embedding method can be used to detect unauthorized use of a digitized signature, and annotate or authenticate binary documents. The paper also presents analysis and discussions on robustness and security issues.

title: Robust error-resilient approach for MPEG video transmission over Internet
abstract: In network delivery of compressed video, packets may be lost if the channel is unreliable. Such losses tend to occur in burst. In this paper, we develop an error resilient video encoding approach to help error concealment at the decoder. We introduce a new block shuffling scheme to isolate erroneous blocks caused by packet losses. And we apply data hiding to add additional protection for motion vectors. The incorporation of these scheme adds little complexity to the standard encoder. Experimental results suggest that our approach can achieve a reasonable quality for packet loss up to 30% over a wide range of video materials.

title: Fingerprinting Curves
abstract: This paper presents a new method for robust data hiding in curves and highlights potential applications including digital fingerprinting of map document for trace and track purposes. We parameterize a curve using the B-spline model and add a spread spectrum sequence to the coordinates of the B-spline control points. In order to achieve robust fingerprint detection, we propose an iterative alignment-minimization algorithm to perform curve registration and deal with the non-uniqueness of B-spline control points. We show through experiments the robustness of our method against various attacks such as collusion, geometric transformations and perturbation, printing-and-scanning, and some of their combinations. We demonstrate the feasibility of our method for fingerprinting topographic maps as well as writings and drawings. The extension from hiding data in 2D topographic maps to 3D elevation data sets is also discussed.

title: Informing the Design of Direct-Touch Tabletops
abstract: Tables provide a large and natural interface for supporting direct manipulation of visual content for human-to-human interactions. Such surfaces also support collaboration, coordination, and parallel problem solving. However, the direct-touch table metaphor also presents considerable challenges, including the need for input methods that transcend traditional mouse- and keyboard-based designs. In this paper, we've designed, implemented, and studied a variety of tabletop user interfaces, interaction techniques, and usage scenarios

title: A Subspace Kernel for Nonlinear Feature Extraction
abstract: Kernel based nonlinear Feature Extraction (KFE) or dimensionality reduction is a widely used preprocessing step in pattern classification and data mining tasks. Given a positive definite kernel function, it is well known that the input data are implicitly mapped to a feature space with usually very high dimensionality. The goal of KFE is to find a low dimensional subspace of this feature space, which retains most of the information needed for classification or data analysis. In this paper, we propose a subspace kernel based on which the feature extraction problem is transformed to a kernel parameter learning problem. The key observation is that when projecting data into a low dimensional subspace of the feature space, the parameters that are used for describing this subspace can be regarded as the parameters of the kernel function between the projected data. Therefore current kernel parameter learning methods can be adapted to optimize this parameterized kernel function. Experimental results are provided to validate the effectiveness of the proposed approach.

title: A Hyperrelational Approach to Integration and Manipulation of Data in Multidatabase Systems
abstract: The issue of interoperability among multiple autonomous databases has attracted a lot of attention from researchers in these years. The past research on this issue can be roughly divided into two main categories: the tightly-integrated approach that integrate databases by building an integrated schema and the loosely-integrated approach that achieves interoperability by using a multidatabase language. Most of the past efforts focused on the issues in the first approach. The problem with the first approach is, however, that it lacks a convenient representation of the integrated schema at the system level and a sound mathematical basis for data manipulation in a multidatabase system. In this paper, we propose to use hyperrelations as a powerful and succinct model for the global level representation of heterogeneous database schemas. A hyperrelation has the structure of a relation, but its contents are the schemas of the semantically equivalent local relations in the databases. With this representation, the metadata of the global database, local databases and the data of these databases are all representable by using the structure of a relation. The impact of such a representation is that all the elegant features of relational systems can be easily extended to multidatabase systems. A hyperrelational algebra is designed accordingly. This algebra is performed at the multidatabase systems (MDBS) level such that query transformation and optimization is supported on a sound mathematical basis. The major contributions of this paper include: (1) Local relations of various schemas (even though they retain information of the same semantics) can be systematically mapped to hyperrelations. As the structure of a hyperrelation is similar to that of a relation, data manipulation and management tasks (such as design of the global query language and the view mechanism) are greatly facilitated. (2) The hyperrelational algebra provides a sound basis for query transformation and optimization in a MDBS.

title: Research Issues in Data Warehousing
abstract: Data warehousing is a booming industry with many interesting research problems. The database research community has concentrated on only a few aspects. In this paper, We summarize the state of the art, suggest architectural extensions and identify research problems in the areas of warehouse modeling and design, data cleansing and loading, data refreshing and purging, metadata management, extensions to relational operators, alternative implementations of traditional relational operators, special index structures and query optimization with aggregates.

title: Nonlinear V1 responses to natural scenes revealed by neural network analysis
abstract: A key goal in the study of visual processing is to obtain a comprehensive description of the relationship between visual stimuli and neuronal responses. One way to guide the search for models is to use a general nonparametric regression algorithm, such as a neural network. We have developed a multilayer feed-forward network algorithm that can be used to characterize nonlinear stimulus-response mapping functions of neurons in primary visual cortex (area V1) using natural image stimuli. The network is capable of extracting several known V1 response properties such as: orientation and spatial frequency tuning, the spatial phase invariance of complex cells, and direction selectivity. We present details of a method for training networks and visualizing their properties. We also compare how well conventional explicit models and those developed using neural networks can predict novel responses to natural scenes.