Scalable video compression is the encoding of a single video sequence into multiple layers, each layer with its own data rate. Because of the computational complexity of full video encryption, partial encryption has emerged as a general trend for both standard and scalable video codecs. Depending on the application, a particular layer of the video stream is chosen for encryption. In some applications, however, more than one video layer may need to be protected. This results in more complicated key management as multiple keys are needed. In this paper, we present an integrated approach to encrypting multiple layers. Our proposal is a prepositioned shared secret scheme that enables the reconstruction of different keys by communicating different activating shares for the same prepositioned information. It presents advantages over three other key management schemes.

With the advent of digital technologies, many new market opportunities have emerged for content owners, content distributors, and consumer electronics/information technology industries.  An essential requirement for developing a thriving marketplace is the protection of copyrighted content in digital form.  There are four major stages in the delivery of content to the consumer:  (1) capturing on digital media, (2) packaging, (3) distribution to home networks, and (4) transfer to the final audio/visual device within the home network.  Entertainment content is of particular importance as it will be in high demand for many years to come.  If an end-to-end security cannot be provided in a digital market, there would be no incentive for content creation.  Lack of new supplies would result in detrimental effects for all the industries involved in the delivery chain.  In this paper, we present the primary means of securing the entertainment content from creation to consumption in an attempt to understand the overall complexity of the problem.

Video streaming, or the real-time delivery of video over a data network, is the underlying technology behind many applications including video conferencing, video-on-demand, and the delivery of educational and entertainment content.  In many applications, particularly ones involving entertainment content, security issues, such as conditional access and copy protection must be addressed. To resolve these security issues, techniques that include encryption and watermarking need to be developed.  Since the video sequences will often be compressed using a scalable compression technique and transported over a lossy packet network using the Internet Protocol (IP), the security techniques must be compatible with the compression method and data transport and be robust to errors. In this paper, we address the issues involved in the watermarking of rate-scalable video streams delivered using a practical network. Watermarking is the embedding of a signal (the watermark) into a video stream that is imperceptible when the stream is viewed but can be detected by a watermark detector.  Many watermarking techniques have been proposed for digital images and video, but the issues of streaming have not been fully investigated.  A review of streaming video is presented, including scalable video compression and network transport, followed by a brief review of video watermarking and the discussion of watermarking streaming video.

A digital home network is a cluster of digital audio/visual (A/V) devices including set-top boxes, TVs, VCRs, DVD players, and general-purpose computing devices such as personal computers. The network may receive copyrighted digital multimedia content from a number of sources. This content may be broadcast via satellite or terrestrial systems, transmitted by cable operators, or made available as prepackaged media (e.g., a digital tape or a digital video disc). Before releasing their content for distribution, the content owners may require protection by specifying access conditions. Once the content is delivered to the consumer, it moves across the home network until it reaches its destination where it is stored or displayed. A copy protection system is needed to prevent unauthorized access to bit streams in transmission from one A/V device to another or while it is in storage on magnetic or optical media. Recently, two fundamental groups of technologies, encryption and watermarking, have been identified for protecting copyrighted digital multimedia content. This paper is an overview of the work done for protecting content owners’ investment in intellectual property.

Digital watermarking research has generally focused upon two classes of watermarks, fragile and robust.  Robust watermarks are designed to be detected even after attempts are made to remove them.  Fragile watermarks are used for authentication purposes and are capable of detecting even minute changes of the watermarked content.  Unfortunately, neither type of watermark is ideal when considering “information preserving” transformations (such as compression) which preserve the meaning or expression of the content and “information altering” transformations (such as feature replacement) which change the expression of the content.  In this paper we describe a semi-fragile watermark for still images that can detect information altering transformations even after the watermarked content is subjected to information preserving alterations.

A digital home network is a cluster of digital audio/visual (A/V) devices including set-top boxes, TVs, VCRs,  DVD players, and general-purpose computing devices such as personal computers.  The network may receive copyrighted digital multimedia content from a number of sources.  This content may be broadcast via satellite or terrestrial systems, transmitted by cable operators, or made available as prepackaged media (e.g., a digital tape or a digital video disc).  Before releasing their content for distribution, the content owners may require protection by specifying access conditions.  Once the content is delivered to the consumer, it moves across home the network until it reaches its destination where it is stored or displayed.  A copy protection system is needed to prevent unauthorized access to bit streams in transmission from one A/V device to another or while it is in storage on magnetic or optical media.  Recently, two fundamental groups of technologies, encryption and watermarking, have been identified for protecting copyrighted digital multimedia content.  This paper is an overview of the work done for protecting content owners’ investment in intellectual property.

Many image watermarks have been proposed to protect intellectual property in an age there digital images may be easily modified and perfectly reproduced. In a fragile marking system, a signal (watermark) is embedded within an image such that subsequent alterations to the watermarked image can be detected with high probability. The insertion of the watermark is perceptually invisible under normal human observation. These types of marks have found applicability in image authentication systems. In this paper we discuss fragile marking systems and their desirable features, common methods of attack, and survey some recent marking systems.

In this paper we will overview the use of data hiding techniques in digital images. In particular we will describe how one can use Steganography to hide information in a digital image. Steganography is related to cryptography and is the basis for many of the digital watermarking techniques currently being developed. The interest in data hiding has risen with the recent activity in digital copyright protection schemes. One way to protect the ownership of a digital image is to secretly embed data in the content of the image identifying the owner. This paper will review recent developments in data hiding, specifically as it pertains to copyright protection of digital images.

Two classes of digital watermarks have been developed to protect the copyright ownership of digital images. Robust watermarks are designed to withstand attacks on an image (such as compression or scaling), while fragile watermarks are designed to detect minute changes in an image. Fragile marks can also identify where an image has been altered. This paper compares two fragile watermarks. The first method uses a hash function to obtain a digest of the image. An altered or forged version of the original image is then hashed and the digest is compared to the digest of the original image. If the image has changed the digests will be different. We will describe how images can be hashed so that any changes can be spatially localized. The second method uses the Variable-Watermark Two-Dimensional algorithm (VW2D) [1]. The sensitivity to changes is user-specific. Either no changes can be permitted (similar to a hard hash function), or an image can be altered and still be labeled authentic. Latter algorithms are known as semi-fragile watermarks.  We will describe the performance of these two techniques and discuss under what circumstances one would use a particular technique.

In this paper we present a novel multiresolution scheme for the detection of spiculated lesions in digital mammograms. First, a multiresolution representation of the original mammogram is obtained using a linear phase nonseparable 2-D wavelet transform. A set of features is then extracted at each resolution in the wavelet pyramid for every pixel. This approach addresses the difficulty of predetermining the neighborhood size for feature extraction to characterize objects that may appear in different sizes. Detection is performed from the coarsest resolution to the finest resolution using a binary tree classifier. This top-down approach requires less computation by starting with the least amount of data and propagating detection results to finer resolutions. Experimental results using the MIAS image database have shown that this algorithm is capable of detecting spiculated lesions of very different sizes at low false positive rates.

In this paper we present new results relative to the “expectation maximization/maximization of the posterior marginals” (EM/MPM) algorithm for simultaneous parameter estimation and segmentation of textured images. The EM/MPM algorithm uses a Markov random field model for the pixel class labels and alternately approximates the MPM estimate of the pixel class labels and estimates parameters of the observed image model.  The goal of the EM/MPM algorithm is to minimize the expected value of the number of misclassified pixels.  We present new theoretical results in this paper which show that the algorithm can be expected to achieve this goal, to the extent that the EM estimates of the model parameters are close to the true values of the model parameters.  We also present new new experimental results demonstrating the performance of the EM/MPM algorithm.

We present a new algorithm for segmentation of textured images using a multiresolution Bayesian approach. The new algorithm uses a multiresolution Gaussian autoregressive (MGAR) model for the pyramid representation of the observed image, and assumes a multiscale Markov random field model for the class label pyramid. Unlike previously proposed Bayesian multiresolution segmentation approaches, which have either used a single-resolution representation of the observed image or implicitly assumed independence between different levels of a multiresolution representation of the observed image, the models used in this paper incorporate correlations between different levels of both the observed image pyramid and the class label pyramid. The criterion used for segmentation is the minimization of the expected value of the number of misclassified nodes in the multiresolution lattice. The estimate which satisfies this criterion is referred to as the “multiresolution maximization of the posterior marginals” (MMPM) estimate, and is a natural extension of the single-resolution “maximization of the posterior marginals” (MPM) estimate. Previous multiresolution segmentation techniques have been based on the maximum a posteriori (MAP) estimation criterion, which has been shown to be less appropriate for segmentation than the MPM criterion. It is assumed that the number of distinct textures in the observed image is known. The parameters of the MGAR model—the means, prediction coefficients, and prediction error variances of the different textures—are unknown. A modified version of the expectation-maximization (EM) algorithm is used to estimate these parameters. The parameters of the Gibbs distribution for the label pyramid are assumed to be known. Experimental results demonstrating the performance of the algorithm are presented.

The use of mathematical morphology in low and mid-level image processing and computer vision applications has allowed the development of a class of techniques for analyzing shape information in monochrome images. In this paper these techniques are extended to color images. We investigate two approaches for \color morphology”: a vector approach, in which color vectors are ranked using a multivariate ranking concept known as reduced ordering, and a component-wise approach, in which grayscale morphological operations are applied to each of the three color component images independently. New vector morphological filtering operations are defined, and a set-theoretic analysis of these vector operations is presented. We also present experimental results comparing the performance of the vector approach and the component-wise approach for two applications: multiscale color image analysis and noise suppression in color images.

Collaborative and distributed applications, such as dynamic coalitions and virtualized grid computing, often require integrating access control policies of collaborating parties. Such an integration must be able to support complex authorization specifications and the fine-grained integration requirements that the various parties may have. In this paper, we introduce an algebra for fine-grained integration of sophisticated policies. The algebra is able to support the specification of a large variety of integration constraints. To assess the expressive power of our algebra, we prove its completeness and minimality. We then propose a framework that uses the algebra for the fine-grained integration of policies expressed in XACML. We also present a methodology for generating the actual integrated XACML policy, based on the notion of Multi-Terminal Binary Decision Diagrams.

When transmitting compressed video over a data network, one has to deal with how channel errors affect the decoding process. This is particularly problematic with data loss or erasures. In this paper we describe techniques to address this problem in the context of networks where channel errors or congestion can result in the loss of entire macroblocks when MPEG video is transmitted. We describe spatial and temporal techniques for the recovery of lost macroblocks. In particular, we develop estimation techniques for the reconstruction of missing macroblocks using a Markov Random Field model. We show that the widely used heuristic motion compensated error concealment technique based on averaging motion vectors is a special case of our estimation technique. We further describe a technique that can be implemented in real-time.