Video access control via multi-level data hiding
The paper proposes novel data hiding algorithms and system design for high quality digital video. Instead of targeting on a single degree of robustness, which results in overestimation and/or underestimation of the noise conditions, we apply multi-level embedding to digital video to achieve more than one level of robustness-capacity tradeoff. In addition, an adaptive technique is proposed to determine how many bits are embedded in each part of the video. Besides user data, control information such as synchronization and the number of hidden user bits are embedded as well. The algorithm can be used for applications such as access control.

title: Security evaluation for communication-friendly encryption of multimedia
abstract: This paper addresses the access control issues unique to multimedia, by using a joint signal processing and cryptographic approach to multimedia encryption. Based on three atomic encryption primitives, we present a systematic study on how to strategically integrate different atomic operations to build a video encryption system. We also propose a set of multimedia-specific security metrics to quantify the security against approximation attacks and to complement the existing notion of generic data security. The resulting system can provide superior performance to both generic encryption and its simple adaptation to video in terms of a joint consideration of security, bitrate overhead, and communication friendliness.

title: Forensic analysis of nonlinear collusion attacks for multimedia fingerprinting
abstract: 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: 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: Classification-based spatial error concealment for images
abstract: This paper presents a new, classification-based spatial error concealment algorithm for images. The proposed scheme takes advantage of two state-of-the-art concealment schemes and adaptively selects a better suitable concealment scheme for each corrupted block. Using a support vector machine (SVM) classifier, our proposed approach outperforms the prior art in terms of the concealment quality and has moderate computational complexity.

title: Data hiding in curves for collusion-resistant digital fingerprinting
abstract: This paper presents a new data hiding method for curves. The proposed algorithm parameterizes a curve using the B-spline model and adds a spread spectrum sequence in the coordinates of the B-spline control points. We demonstrate through experiments the robustness of the proposed data hiding algorithm against printing-and-scanning and collusions, and show its feasibility for collusion-resistant fingerprinting of topographic maps as well as writings/drawings from pen-based input devices.

title: Query optimization for selections using bitmaps
abstract: Bitmaps are popular indexes for data warehouse (DW) applications and most database management systems offer them today. This paper proposes query optimization strategies for selections using bitmaps. Both continuous and discrete selection criteria are considered. Query optimization strategies are categorized into static and dynamic. Static optimization strategies discussed are the optimal design of bitmaps, and algorithms based on tree and logical reduction. The dynamic optimization discussed is the approach of inclusion and exclusion for both bit-sliced indexes and encoded bitmap indexes.

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: CSIRO INEX experiments: XML Search using PADRE
abstract: This paper reports on the CSIRO group's participation in INEX. We indexed documents and document fragments using PADRE, the core of CSIRO's Panoptic Enterprise Search Engine. A query translator converts the INEX topics into queries containing selection and projection constraints for the results. Answers are extracted from ranked documents and document fragments based on the projection constraints in the query.

title: Supporting Group-By and Pipelining in Bitmap-Enabled Query Processors
abstract: The Group-By operation is widely used in relational Data Warehouses for the aggregation and presentation of results according to user-defined criteria. Rearranging Group-By and join operations has been proposed as an optimization technique that reduces the size of the input relation. Pipelining is another optimization technique that promotes intra-query parallelism by transfering intermediate results to the next operation without materializing them. In Data Warehouses and environments in which exploratory on-line queries are common, pipelining can be used both for optimizing joins and for the reduction of response time by presenting partial results to the user. Efficient pipelining often depends on the order of the intermediate results. Order-dependent operations, such as Group-By, typically require the complete result set before ordering it, thereby making efficient pipelining impossible. In this paper we exploit bitmap indexing to implement Group-By in a manner that can be used for pipelining. Algorithms are presented for different assumptions about buffer availability and for different sort orders.

title: EITH - A Unifying Representation for Database Schema and Application Code in Enterprise Knowledge Extraction
abstract: The integration of heterogeneous legacy databases requires understanding of database structure and content. We previously developed a theoretical and software infrastructure to support the extraction of schema and business rule information from legacy sources, combining database reverse engineering with semantic analysis of associated application code (DRE/SA). In this paper, we present a compact formalism called EITH that unifies the representation of database schema and application code. EITH can be efficiently derived from various types of schema representations, particularly the relational model, and supports comparison of a wide variety of schema and code constructs to enable interoperation. Unlike UML or E/R diagrams, for example, EITH has compact notation, is unambiguous, and uses a small set of efficient heuristics. We show how EITH is employed in the context of SEEK, using a construction project management example. We also show how EITH can represent various structures in relational databases, and can serve as an efficient representation for E/R diagrams. This suggests that EITH can support efficient matching of more complex, hierarchical structures via indexed tree representations, without compromising the EITH design philosophy or formalism.