An attribute-based encryption scheme capable of handling multiple authorities was recently proposed by Chase. The scheme is built upon a single-authority attribute-based encryption scheme presented earlier by Sahai and Waters. Chase's construction uses a trusted central authority that is inherently capable of decrypting arbitrary ciphertexts created within the system. We present a multi-authority attribute-based encryption scheme in which only the set of recipients defined by the encrypting party can decrypt a corresponding ciphertext. The central authority is viewed as 'honest-but-curious': on the one hand, it honestly follows the protocol, and on the other hand, it is curious to decrypt arbitrary ciphertexts thus violating the intent of the encrypting party. The proposed scheme, which like its predecessors relies on the Bilinear Diffie-Hellman assumption, has a complexity comparable to that of Chase's scheme. We prove that our scheme is secure in the selective ID model and can tolerate an honest-but-curious central authority.

The aim of this review is to study the methods of steganography using the video file as a cover carrier. The steganography is the art of protecting the information through embedding data in medium carrier, for instants this study illustrate historically this art, as well as the study describes methods as a review for this art in the video file. The video based steganography can be used as one video file, separated images in frames or images and audio files. Since that, the use of the video based steganography can be more eligible than other multimedia files. As a result of this study, the video based steganography has been discussed and the advantages of using the video file as a cover carrier for steganography have been proposed.

The paper presents a system for automatic video surveillance based on paired cameras in a stereoscopic setup. The system combines motion based object segmentation with tracking, and integrates depth information to achieve robust performance. The methodology encompasses object segmentation based on a class of probabilistic neural networks, shadow removal, computation of a depth map, and object tracking based on an extended set of MPEG-7like feature descriptors including intensity, color, shape, motion and depth. To evaluate the approach, experiments were conducted on a set of outdoor sequences containing both rigid objects and moving persons. The results presented in the paper indicate that the proposed approach is able to achieve accurate segmentation and tracking and handle occlusions efficiently.

Autonomous video surveillance systems typically consist of several functional modules working in concert. These modules perform specialized tasks including motion detection, separation of the foreground and background, depth estimation, object tracking, feature estimation, and behavioral analysis. Computational overhead and redundancy may result from designing each module individually, as each module may incorporate different variety of techniques and algorithms. This paper presents the design of a surveillance system that uses an optical flow algorithm throughout. We consider the capabilities, solutions, and limitations of this design. Additionally, an evaluation of the performance of optical flow in specific situations, such as depth estimation, rigid and non-rigid classification, segmentation, and tracking, is provided. The main contribution of this work is a new system-level architecture based on a single key algorithm (optical flow) for the entire video surveillance system.

A novel scheme for securing biometric templates of variable size and order is proposed. The proposed scheme is based on a new similarity measure approach, namely the set intersection, which strongly resembles the methodology used in most of the current state-of-the-art biometrics matching systems. The applicability of the new scheme is compared with that of the existing principal schemes, and it is shown that the new scheme has definite advantages over the existing approaches. The proposed scheme is analyzed both in terms of security and performance.

The field of automated video surveillance has experienced increased research interest due to falling costs of video sensors, increasing security concerns, and the need for improved algorithm for extracting high-level information from video sequences. The analysis of human activities and their environment within the context of security provides information enabling the proactive identification of anomalous behavior. This makes human detection a prerequisite for the automatic extraction of higher level information, such as the recognition of the activities of individual humans. In this paper, we approach the challenge of detecting humans within video sequences as a classification task; moving objects in the foreground are either human or non-human. The classification approach presented in this work is based on motion (periodic motion detection), appearance (skin color detection), and shape (MPEG-7 shape descriptors). A modular infrastructure for data collection, object instantiation, and tracking was also implemented.

A novel scheme for securing biometric templates of variable size and order is proposed. The proposed scheme is based on new similarity measure approach, namely the set intersection, which strongly resembles the methodology used in most current state-of-the-art biometrics matching systems. The applicability of the new scheme is compared with that of the existing principal schemes, and it is shown that the new scheme has clear advantages over the existing approaches.

This paper presents a novel background modeling and subtraction approach for video object segmentation. A neural network (NN) architecture is proposed to form an unsupervised Bayesian classifier for this application domain. The constructed classifier efficiently handles the segmentation in natural-scene sequences with complex background motion and changes in illumination. The weights of the proposed NN serve as a model of the background and are temporally updated to reflect the observed statistics of background. The segmentation performance of the proposed NN is qualitatively and quantitatively examined and compared to two extant probabilistic object segmentation algorithms, based on a previously published test pool containing diverse surveillance-related sequences. The proposed algorithm is parallelized on a subpixel level and designed to enable efficient hardware implementation.

In this work we propose a novel type of digital video encryption that has several advantages over other currently available digital video encryption schemes. We also present an extended classification of digital video encryption algorithms in order to clarify these advantages. We analyze both security and performance aspects of the proposed method, and show that the method is efficient and secure from a cryptographic point of view. Even though the method is currently feasible only for a certain class of video sequences and video codecs, the method is promising and future investigations might reveal its broader applicability. Finally, we extend our approach into a novel type of digital video steganography where it is possible to disguise a given video with another video.

Object segmentation from a video stream is an essential task in video processing and forms the foundation of scene understanding, object-based video encoding (e.g. MPEG4), and various surveillance and 2D-to-pseudo-3D conversion applications. Many segmentation approaches are pixel-based and suffer from noise in the segmentation results, due to the fact that these approaches do not exploit spatial information. Morphological post processing is typically used to enhance the segmentation results. Here, an alternative post-processing approach is presented. The proposed approach is inspired by well-known aspects of the primate visual system function. The approach is particularly suitable for use with the probabilistic segmentation algorithms and allows for efficient neural-network- based implementation.

A fundamental problem in computer vision is caused by the projection of a three-dimensional world onto one or more two-dimensional planes. As a result, methods for extracting regions of interest (ROIs) have certain limitations that cannot be overcome with traditional techniques that only utilize a single projection of the image. For example, while it is difficult to distinguished two overlapping, homogeneous regions with a single intensity or color image, depth information can usually easily be used to separate the regions. In this paper we present an extension to an existing saliency-based ROI extraction method. By adding depth information to the existing method many previously difficult scenarios can now be handled. Experimental results show consistently improved ROI segmentation.

Background modelling Neural Networks (BNNs) represent an approach to motion based object segmentation in video sequences. BNNs are probabilistic classifiers with non-parametric, kernel-based estimation of the underlying probability density functions. The paper presents an enhancement of the methodology, introducing automatic estimation and adaptation of the kernel width. The proposed enhancement eliminates the need to determine kernel width empirically. The selection of a kernel-width appropriate for the features used for segmentation is critical to achieving good segmentation results. Thus, the improvement results in a segmentation methodology truly general in terms of features used for segmentation.