When compressed video is transmitted through a data network, such as an ATM or a wireless network, data can be lost due to channel errors and/or congestion. Techniques that post-process the received video and conceal the errors in real-time are needed. In this paper we describe an implementation of error concealment techniques on the Texas Instruments TMS320C6201 (‘C6201) digital signal processor.

In this paper, we present a new wavelet based rate scalable video compression algorithm. We shall refer to this new technique as the Scalable Adaptive Motion COmpensated Wavelet (SAMCoW) algorithm. SAMCoW uses motion compensation to reduce temporal redundancy. The prediction error frames and the intra-coded frames are encoded using an approach similar to the embedded zerotree wavelet (EZW) coder. observed. An adaptive motion compensation (AMC) scheme is described to address error propagation problems. We show that using our AMC scheme the quality of the decoded video can be maintained at various data rates. correlation. large transitions,  it is highly likely for the luminance signal to have large transitions. We also describe an EZW approach that exploits the interdependency between color components in the luminance/chrominance color space. We show that in addition to providing a wide range of rate scalability, our encoder achieves comparable performance to the more traditional hybrid video coders, such as MPEG1 and H.263. Furthermore, our coding scheme allows the data rate to be dynamically changed during decoding, which is very appealing for network oriented applications.

Color embedded image compression is investigated by means of a set of core experiments that seek to evaluate the advantages of various color transformations, spatial orientation trees and the use of monochrome embedded coding schemes such as EZW and SPIHT. In order to take advantage of the interdependencies of the color components for a given color space, two new spatial orientation trees that relate frequency bands and color components are investigated.

In this paper, we describe a unique new paradigm for video database management known as ViBE (Video Indexing and Browsing Environment). ViBE is a browseable/searchable paradigm for organizing video data containing a large number of sequences. The system first segments video sequences into shots by using a new feature vector known as the Generalized Trace obtained from the DC-sequence of the compressed data. Each video shot is then represented by a hierarchical structure known as the shot tree. The shots are then classified into pseudo-semantic classes that describe the shot content. Finally, the results are presented to the user in an active browsing environment using a similarity pyramid data structure. The similarity pyramid allows the user to view the video database at various levels of detail. The user can also define semantic classes and reorganize the browsing environment based on relevance feedback. We describe how ViBE performs on a database of MPEG sequences.

In this paper we extend the shot transition detection component of the ViBE video database system to include gradual scene changes. ViBE (Video Indexing and Browsing Environment), a browseable/searchable paradigm for organizing video data containing a large number of sequences, is being developed at Purdue as a testbed to explore ideas and concepts in video databases. We also present results on the performance of our cut detection algorithm using a large test set. The performance of two other techniques are compared against our method.

Pseudo-semantic labeling represents a novel approach for automatic content description of video. This information can be used in the context of a video database to improve browsing and searching. In this paper we describe our work on using face detection techniques for pseudo-semantic labeling. We present our results using a database of MPEG sequences.

In this paper, we describe a unique new paradigm for video database management known as ViBE (Video Indexing and Browsing Environment). ViBE is a browseable/searchable paradigm for organizing video data containing a large number of sequences. We describe how ViBE performs on a database of MPEG sequences.

In this paper I will not discuss the research frontiers of image and video databases but who will be the users of these systems. Questions that have not been adequately addressed by the research community is who are the users and what do they really want these systems to do? The purpose of this paper is to be controversial and to engage a debate within the research community as to where the real applications of our work lie. It should be noted that the author does not agree with every point made in this paper.

Security is an increasingly important attribute for multimedia applications that require prevention of unauthorized access to copyrighted data.  Two approaches have been used to protect scalable video content in distribution:  Partial encryption and progressive encryption.  Partial encryption provides protection for only selected portions of the video.  Progressive encryption allows transcoding with simple packet truncation, and eliminates the need to decrypt the video packets at intermediate network nodes with low complexity.  Centralized Key Management with Secret Sharing (CKMSS) is a recent approach in which the group manager assigns unique secret shares to the nodes in the hierarchical key distribution tree.  It allows the reconstruction of different keys by communicating different activating shares for the same prepositioned information.  Once the group key is established,  it is used until a member joins/leaves the multicast group or periodic rekeying occurs.  In this paper, we will present simulation results regarding the communication and processing requirements of the CKMSS scheme applied to scalable video.  In particular, we have measured the rekey message size and the processing time needed by the server for each join/leave request and periodic rekey event.

Digital multimedia content is delivered to homes via the Internet, satellite, terrestrial and cable net- works. Scrambling is a common approach used by conditional access systems to prevent unauthorized access to au- dio/visual data. The descrambling keys are securely distributed to the receivers in the same transmission channel. Their protection is an important part of the key management problem. Although public-key cryptography provides a viable solution, alternative methods are sought for economy and efficiency. Message authentication is an important objective of information security in modern electronic distribution networks. This objective is met by providing the receiver of a message an assurance of the sender’s identity. As physical protection such as sealed envelopes is not possible for messages expressed as binary sequences, digital tools have been developed using cryptography. A major limitation of all cryptographic methods for message authentication lies in their use of algorithms with fixed symmetric or public keys. This paper presents a key transport protocol based on se- cret sharing. Conditional access and message authentication are two important application areas for which the advantages of the proposed protocol are discussed. The protocol eliminates the need for a cipher, yet effectively combines the advantages of symmetric and public-key ciphers. It can be used to build a new key management scheme that allows the service providers to generate different keys for different sets of receivers, and to renew these keys in a convenient way.

A fraudster can be an impersonator or a swindler. An impersonator is an illegitimate user who steals resources from the victims by “taking over” their accounts. A swindler is a legitimate user who intentionally harms the system or other users by deception. Previous research efforts in fraud detection concentrate on identifying frauds caused by impersonators. Detecting frauds conducted by swindlers is a challenging issue. We propose an architecture to catch swindlers. It consists of four components: profile-based anomaly detector, state transition analysis, deceiving intention predictor, and decision-making component. Profile-  based anomaly detector outputs fraud confidence indicating the possibil-  ity of fraud when there is a sharp deviation from usual patterns. State transition analysis provides state description to users when an activity results in entering a dangerous state leading to fraud. Deceiving inten-  tion predictor discovers malicious intentions. Three types of deceiving intentions, namely uncovered deceiving intention, trapping intention, and illusive intention, are defined. A deceiving intention prediction algorithm is developed. A user-configurable risk evaluation function is used for decision making. A fraud alarm is raised when the expected risk is greater than the fraud investigation cost.

Software selection is an important consideration in risk management for information security. Additionally, the underlying robustness and security of a technology under consideration has become increasingly important in total cost of ownership and other calculations of business value. Open source software is often touted as being robust to many of the problems that seem to plague so-called “proprietary” or non-open source software. This study seeks to empirically investigate, from an information security perspective specific security characteristics of open source software compared to those of proprietary software. Software vulnerability data spanning several years were collected and analyzed to determine if significant differences exist in terms of inter-arrival times of published vulnerabilities, median time to release ‘fixes’ (commonly referred to as patches), type of vulnerability reported and the respective severity of the vulnerabilities. It appears that both open source and proprietary software are each likely to report similar vulnerabilities and that open source software is quicker in releasing patches for problems identified in their software. However, comparisons of yearly statistics reveal improvements in the performance of proprietary software companies. This suggests that they are quickly realizing the competition presented by the open source software community.

Genetic algorithms (GAs) are routinely used to search problem spaces of interest. A lesser known but growing group of applications of GAs is the modeling of so-called “evolutionary processes”, for example, organizational learning and group decision-making. Given such an application, we show it is possible to compute the likely GA parameter settings given observed populations of such an evolutionary process. We examine the parameter estimation process using estimation procedures for learning hidden Markov models, with mathematical models that exactly capture expected GA behavior. We then explore the sampling distributions relevant to this estimation problem using an experimental approach.

Groups using group decision support systems (GDSS) to address particular tasks can be viewed as performing a search. Such tasks involve arriving at a solution or decision within the context of a complex search space, warranting the use of computerized decision support tools. The type of search undertaken by the groups appears to be a form of adaptive, rather than enumerative, search. Recently, efforts have been made to incorporate this adaptation into an analytical model of GDSS usage. One possible method for incorporating adaptation into an analytical model is to use an evolutionary algorithm, such as a genetic algorithm (GA), as an analogy for the group problem-solving process. In this paper, a test is made to determine whether GDSS behaves similarly to a GA process utilizing rank selection, uniform crossover, and uniform mutation operators. A Markov model for GAs is used to make this determination. Using GDSS experimental data, the best-fit transition probabilities are estimated and various hypotheses regarding the relation of GA parameters to GDSS functionality are proposed and tested. Implications for researchers in both GAs and group decision support systems are discussed.

We propose modeling Group Support System (GSS) search tasks with Genetic Algorithms. Using explicit mathematical models for Genetic Algorithms (GAs), we show how to estimate the underlying GA parameters from an observed GSS solution path.  Once these parameters are estimated, they may be related to GSS variables such as group composition and membership, leadership presence, the specific GSS tools available, incen-  tive structure, and organizational culture. The estimated Genetic Algorithm parameters can be used with the mathematical models for GAs to compute or simulate expected GSS pro-  cess outcomes.