Survey on: Multimedia Content Protection using Cloud

There is need for large scale multimedia content protection system. There are varying workloads for which cloud infrastructure provide cost efficiency, rapid development and scalability. Data protection along with security whole together contribute to the success of cloud. Security is very important in today�s online world. It is widely accepted that cloud computing has the unrealized ability to make privacy disable. The cloud is been chosen because it provides some security features. The greatest challenge is to process data securely in cloud. One of the factor leading to high performance in cloud is nothing but the security. For the protection purpose, a system for multimedia content protection on cloud infrastructure is presented. The system can be used to protect various multimedia contents such as 2D video and 3D videos, graphics which is animated, images, audios clips etc. Multimedia is the combination of data, text, image, audio, or video in a single application. The system can be deployed on both public cloud and private cloud. Two major components to be considered are method to create signatures of 3D videos and matching engine for multimedia objects. Depth signals are captured from 3D videos. This system detects the duplicated multimedia content that is copyright material in an online environment

ONLINE DETECTION OF COPYRIGHT PROTECTION SYSTEM FOR VIDEOS STREAMS USING CLOUD

With Digital revolution, content creator space has increased and many content owners create video contents and publish on streaming sites like YouTube. These contents can be stolen on internet and published in some other sites completely or partially. To detect this online copy, a copy right protection system is need which can detect if content is copied to some other site and its URL and the percentage of copy. If this information is available, the content owner can sue the copier and the sites hosting copied information. With increasing lot of online videos there must a way to identify the copy with a reasonable amount of time. In this paper, we propose a copy right detection system which works fast and on online

Aggregated search: a new information retrieval paradigm

International audienceTraditional search engines return ranked lists of search results. It is up to the user to scroll this list, scan within different documents and assemble information that fulfill his/her information need. Aggregated search represents a new class of approaches where the information is not only retrieved but also assembled. This is the current evolution in Web search, where diverse content (images, videos, ...) and relational content (similar entities, features) are included in search results. In this survey, we propose a simple analysis framework for aggregated search and an overview of existing work. We start with related work in related domains such as federated search, natural language generation and question answering. Then we focus on more recent trends namely cross vertical aggregated search and relational aggregated search which are already present in current Web search

Heap-based Algorithms to Accelerate Fingerprint Matching on Parallel Platforms

Nowadays, fingerprint is the most used biometric trait for individuals identification. In this area, the state-of-the-art algorithms are very accurate, but when the database contains millions of identities, an acceleration of the algorithm is required. From these algorithms, Minutia Cylinder-Code (MCC) stands out for its good results in terms of accuracy, however its efficiency in computational time is not high. In this work, we propose to use two different parallel platforms to accelerate fingerprint matching process by using MCC: (1) a multi-core server, and (2) a Xeon Phi coprocessor. Our proposal is based on heaps as auxiliary structure to process the global similarity of MCC. As heap-based algorithms are exhaustive (all the elements are accessed), we also explored the use an indexing algorithm to avoid comparing the query against all the fingerprints of the database. Experimental results show an improvement up to 97.15x of speed-up, which is competitive compared to other state-of-the-art algorithms in GPU and FPGA. To the best of our knowledge, this is the first work for fingerprint identification using a Xeon Phi coprocessor.Instituto de Investigación en Informátic

Heap-based Algorithms to Accelerate Fingerprint Matching on Parallel Platforms

Nowadays, fingerprint is the most used biometric trait for individuals identification. In this area, the state-of-the-art algorithms are very accurate, but when the database contains millions of identities, an acceleration of the algorithm is required. From these algorithms, Minutia Cylinder-Code (MCC) stands out for its good results in terms of accuracy, however its efficiency in computational time is not high. In this work, we propose to use two different parallel platforms to accelerate fingerprint matching process by using MCC: (1) a multi-core server, and (2) a Xeon Phi coprocessor. Our proposal is based on heaps as auxiliary structure to process the global similarity of MCC. As heap-based algorithms are exhaustive (all the elements are accessed), we also explored the use an indexing algorithm to avoid comparing the query against all the fingerprints of the database. Experimental results show an improvement up to 97.15x of speed-up, which is competitive compared to other state-of-the-art algorithms in GPU and FPGA. To the best of our knowledge, this is the first work for fingerprint identification using a Xeon Phi coprocessor.Instituto de Investigación en Informátic