20,908 research outputs found
e-Counterfeit: a mobile-server platform for document counterfeit detection
This paper presents a novel application to detect counterfeit identity
documents forged by a scan-printing operation. Texture analysis approaches are
proposed to extract validation features from security background that is
usually printed in documents as IDs or banknotes. The main contribution of this
work is the end-to-end mobile-server architecture, which provides a service for
non-expert users and therefore can be used in several scenarios. The system
also provides a crowdsourcing mode so labeled images can be gathered,
generating databases for incremental training of the algorithms.Comment: 6 pages, 5 figure
A Personalized and Scalable Machine Learning-Based File Management System
In this work, we present a hybrid image and document filing system that we have built. When a user wants to store a file in the system, it is processed to generate tags using an appropriate open-source machine learning system. Presently, we use OpenCV and Tesseract OCR for tagging files. OpenCV recognizes objects in the images and TesserAct recognizes text in the image. An image file is processed for object recognition using OpenCV as well for text/captions process using TesserAct, which are used for tagging the file. All other files are processed using Tesseract only for generating tags. The user can also enter their own tags. A database system has been built that stores tags and the image path. Every file is stored with its owner identification and it is time-stamped. The system has a client-server architecture and can be used for storing and retrieving a large number of files. This is a highly scalable system
Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database
In this paper we present a novel architecture for storing visual data.
Effective storing, browsing and searching collections of images is one of the
most important challenges of computer science. The design of architecture for
storing such data requires a set of tools and frameworks such as SQL database
management systems and service-oriented frameworks. The proposed solution is
based on a multi-layer architecture, which allows to replace any component
without recompilation of other components. The approach contains five
components, i.e. Model, Base Engine, Concrete Engine, CBIR service and
Presentation. They were based on two well-known design patterns: Dependency
Injection and Inverse of Control. For experimental purposes we implemented the
SURF local interest point detector as a feature extractor and -means
clustering as indexer. The presented architecture is intended for content-based
retrieval systems simulation purposes as well as for real-world CBIR tasks.Comment: Accepted for the 14th International Conference on Artificial
Intelligence and Soft Computing, ICAISC, June 14-18, 2015, Zakopane, Polan
VirtualIdentity : privacy preserving user profiling
User profiling from user generated content (UGC) is a common practice that supports the business models of many social media companies. Existing systems require that the UGC is fully exposed to the module that constructs the user profiles. In this paper we show that it is possible to build user profiles without ever accessing the user's original data, and without exposing the trained machine learning models for user profiling - which are the intellectual property of the company - to the users of the social media site. We present VirtualIdentity, an application that uses secure multi-party cryptographic protocols to detect the age, gender and personality traits of users by classifying their user-generated text and personal pictures with trained support vector machine models in a privacy preserving manner
Control What You Include! Server-Side Protection against Third Party Web Tracking
Third party tracking is the practice by which third parties recognize users
accross different websites as they browse the web. Recent studies show that 90%
of websites contain third party content that is tracking its users across the
web. Website developers often need to include third party content in order to
provide basic functionality. However, when a developer includes a third party
content, she cannot know whether the third party contains tracking mechanisms.
If a website developer wants to protect her users from being tracked, the only
solution is to exclude any third-party content, thus trading functionality for
privacy. We describe and implement a privacy-preserving web architecture that
gives website developers a control over third party tracking: developers are
able to include functionally useful third party content, the same time ensuring
that the end users are not tracked by the third parties
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