Performance Study on Multimedia Fingerprinting Employing Traceability Codes
Digital fingerprinting is a tool to protect multimedia content from illegal redistribution by uniquely marking copies of the content distributed to each user. Collusion attack is a powerful attack whereby several differently-fingerprinted copies of the same content are combined together to attenuate or even remove the fingerprint. Coded fingerprinting is one major category of fingerprinting techniques against collusion. Many fingerprinting codes are proposed with tracing capability and collusion resistance, such as Traceability (TA) codes and Identifiable Parent Property (IPP) codes. Most of these works treat the important embedding issue in terms of a set of simplified and abstract assumptions, and they do not examine the end-to-end performance of the coded multimedia fingerprinting. In this paper we jointly consider the coding and embedding issues and examine the collusion resistance of coded fingerprinting systems with various code parameters. Our results show that TA codes generally offer better collusion resistance than IPP codes, and a TA code with a larger alphabet size and a longer code length is preferred.

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: 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: 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: 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: Myriad: An Architecture for Contextualized Information Retrieval and Delivery
abstract: Users’ information needs are largely driven by the context in which they make their decisions. This context is dynamic. It includes the users’ characteristics, their current domain of application, the tasks they commonly perform and the device they are currently using. This context is also evolving. When one information need is satisfied, another is likely to emerge. An information access system must, therefore, be able to track this dynamic and evolving context, and exploit it to retrieve actionable information from appropriate sources and deliver it in a form suitable for the current situation. This paper presents a generic architecture that supports the construction of information retrieval and delivery systems that make use of context. The architecture, called Myriad, includes an adaptive virtual document planner, and explicit, dynamic representations of the user’s current context.

title: A Direct Method for Building Sparse Kernel Learning Algorithms
abstract: Many kernel learning algorithms, including support vector machines, result in a kernel machine, such as a kernel classifier, whose key component is a weight vector in a feature space implicitly introduced by a positive definite kernel function. This weight vector is usually obtained by solving a convex optimization problem. Based on this fact we present a direct method to build sparse kernel learning algorithms by adding one more constraint to the original convex optimization problem, such that the sparseness of the resulting kernel machine is explicitly controlled while at the same time performance is kept as high as possible. A gradient based approach is provided to solve this modified optimization problem. Applying this method to the support vectom machine results in a concrete algorithm for building sparse large margin classifiers. These classifiers essentially find a discriminating subspace that can be spanned by a small number of vectors, and in this subspace, the different classes of data are linearly well separated. Experimental results over several classification benchmarks demonstrate the effectiveness of our approach.

title: Question-driven classification of retrieved documents
abstract: Many existing information access systems deliver ranked lists of documents in response to users' queries. Some systems also endeavour to represent some of the various types of relationships that can exist between documents. However, few systems provide effective mechanisms to help users discover useful information within the set of retrieved documents. In this paper, we present a question-driven approach to delivering retrieved documents in an attempt to organize them in a way that is closer to the user's mental representation of the expected answer. In our proposed approach, retrieved documents are dynamically classified into categories; an appropriate classification scheme is selected by a user on the basis of his own understanding of the information need. Experimental results show that users are more satisfied with such a directed categorization than with a list of retrieved documents.

title: Balancing productivity and consumer satisfaction for profitability: Statistical and fuzzy regression analysis
abstract: This paper examines the relationships among productivity, consumer satisfaction and profitability using the conventional statistical regression and the new fuzzy regression approaches. For service firms in the context of Hong Kong, we verify the profit hypothesis that individually both productivity and consumer satisfaction are positively linked to profitability as well as the tradeoff hypothesis that aggregately there are negative interactions between productivity and consumer satisfaction for enhancing profitability. Hence service firms should balance their efforts in productivity and consumer satisfaction, possibly by employing appropriate information technologies to improve productivity while without hurting consumer satisfaction, to optimize their profitability. The study takes advantage of the Hong Kong Consumer Satisfaction Index and deliberately focuses on total rather than partial productivity. Several models are first estimated using the ordinary least squares (OLS) method and the results generally support the two hypotheses, but the OLS approach also leaves two puzzles that estimates of the regression coefficients are: (1) not significant before considering the interactions between consumer satisfaction and productivity but significant after introducing the interaction term, and (2) significant although sample data for productivity and the interaction term are highly correlated. These puzzles, together with the observed imprecision in productivity and profitability measurements and especially the subjectivity in measuring consumer satisfaction, lead us to adopt the fuzzy linear regression (FLR) techniques to further examine the two hypotheses. The popular FLR model continues to favor our research hypotheses but fail to offer any additional insights into the examined relationships over the OLS models. We then propose a revised FLR model which, in addition to reconfirming the hypotheses, does help to explain the encountered puzzles and fuzziness, and hence suggests an encouraging methodology for marketing.