Distortion compensated lookup-table embedding: joint security and robustness enhancement for quantization-based data hiding
Data embedding mechanism used for authentication applications should be secure in order to prevent an adversary from forging the embedded data at his/her will. Meanwhile, semi-fragileness is often preferred to allow for distinguishing content changes versus non-content changes. In this paper, we focus on jointly enhancing the robustness and security of the embedding mechanism, which can be used as a building block for authentication. The paper presents analysis showing that embedding through a look-up table (LUT) of non-trivial run that maps quantized multimedia features randomly to binary data offers a probability of detection error considerably smaller than that of the traditional quantization embedding. We quantify the security strength of LUT embedding and enhance its robustness through distortion compensation. We introduce a combined security and capacity measure and show that the proposed distortion compensated LUT embedding provides joint enhancement of security and robustness over the traditional quantization embedding.

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: 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: 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: 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: 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: Using Document Relationships for Better Answers
abstract: In most retrieval systems the answer to a query is a ranked list of documents. There is little information about the ranking and no support for exploring the relationships that may exist between the documents. In this paper we consider the use of clustering answers to better support users satisfying their information needs. We show how clustering reflects the nature of some information needs, and how the clustering can be used to find more relevant documents than would be the case using simple lists. This work contributes to our approach of building answers to information needs, rather than simply providing lists.

title: Do security toolbars actually prevent phishing attacks?
abstract: Security toolbars in a web browser show security-related information about a website to help users detect phishing attacks. Because the toolbars are designed for humans to use, they should be evaluated for usability -- that is, whether these toolbars really prevent users from being tricked into providing personal information. We conducted two user studies of three security toolbars and other browser security indicators and found them all ineffective at preventing phishing attacks. Even though subjects were asked to pay attention to the toolbar, many failed to look at it; others disregarded or explained away the toolbars' warnings if the content of web pages looked legitimate. We found that many subjects do not understand phishing attacks or realize how sophisticated such attacks can be.

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: Generating Personal Travel Guides from Discourse Plans
abstract: This paper describes a system that delivers travel guides tailored to individual needs. It does so by integrating a discourse planner with a system for querying the web and generating synthesised web pages using document prescriptions. We show by way of example how a user model can lead to a personal travel guide, and show what this might look like in different media. We briefly describe the investigations we are undertaking to determine the utility of such approaches.

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.