99,488 research outputs found

    Source identification for mobile devices, based on wavelet transforms combined with sensor imperfections

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    One of the most relevant applications of digital image forensics is to accurately identify the device used for taking a given set of images, a problem called source identification. This paper studies recent developments in the field and proposes the mixture of two techniques (Sensor Imperfections and Wavelet Transforms) to get better source identification of images generated with mobile devices. Our results show that Sensor Imperfections and Wavelet Transforms can jointly serve as good forensic features to help trace the source camera of images produced by mobile phones. Furthermore, the model proposed here can also determine with high precision both the brand and model of the device

    An Analysis of Optical Contributions to a Photo-Sensor's Ballistic Fingerprints

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    Lens aberrations have previously been used to determine the provenance of an image. However, this is not necessarily unique to an image sensor, as lens systems are often interchanged. Photo-response non-uniformity noise was proposed in 2005 by Luk\'a\v{s}, Goljan and Fridrich as a stochastic signal which describes a sensor uniquely, akin to a "ballistic" fingerprint. This method, however, did not account for additional sources of bias such as lens artefacts and temperature. In this paper, we propose a new additive signal model to account for artefacts previously thought to have been isolated from the ballistic fingerprint. Our proposed model separates sensor level artefacts from the lens optical system and thus accounts for lens aberrations previously thought to be filtered out. Specifically, we apply standard image processing theory, an understanding of frequency properties relating to the physics of light and temperature response of sensor dark current to classify artefacts. This model enables us to isolate and account for bias from the lens optical system and temperature within the current model.Comment: 16 pages, 9 figures, preprint for journal submission, paper is based on a thesis chapte

    Monitoring of Plant Species and Communities on Coastal Cliffs: Is the Use of Unmanned Aerial Vehicles Suitable?

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    Cliffs are reservoirs of biodiversity; therefore, many plant species and communities of inland and coastal cliffs are protected by Council Directive 92/43/EEC (European Economic Community), and their monitoring is mandatory in European Union countries. Surveying plants on coastal cliff by traditional methods is challenging and alternatives are needed. We tested the use of a small Unmanned Aerial Vehicle (UAV) as an alternative survey tool, gathering aerial images of cliffs at Palinuro Cape (Southern Italy). Four photo-interpreters analysed independently the derived orthomosaic and plotted data needed for the monitoring activity. Data showed to be not affected by photo-interpreters and reliable for the prescribed monitoring in the European Union (EU). Using the GIS analysis tools, we were able to: (a) recognise and map the plant species, (b) derive and measure the area of distribution on the cliff of habitat and species, and (c) count Eokochia saxicola individuals and gather quantitative data on their projected area. Quality of the images represented the main constraint, but incoming technological improvements of sensors and UAVs may overcome this problem. Overall results support the use of UAVs as an affordable and fast survey technique that can rapidly increase the number of studies on cliff habitats and improve ecological knowledge on their plant species and communitie

    PlaceRaider: Virtual Theft in Physical Spaces with Smartphones

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    As smartphones become more pervasive, they are increasingly targeted by malware. At the same time, each new generation of smartphone features increasingly powerful onboard sensor suites. A new strain of sensor malware has been developing that leverages these sensors to steal information from the physical environment (e.g., researchers have recently demonstrated how malware can listen for spoken credit card numbers through the microphone, or feel keystroke vibrations using the accelerometer). Yet the possibilities of what malware can see through a camera have been understudied. This paper introduces a novel visual malware called PlaceRaider, which allows remote attackers to engage in remote reconnaissance and what we call virtual theft. Through completely opportunistic use of the camera on the phone and other sensors, PlaceRaider constructs rich, three dimensional models of indoor environments. Remote burglars can thus download the physical space, study the environment carefully, and steal virtual objects from the environment (such as financial documents, information on computer monitors, and personally identifiable information). Through two human subject studies we demonstrate the effectiveness of using mobile devices as powerful surveillance and virtual theft platforms, and we suggest several possible defenses against visual malware
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