‘What we do is really to make the fingerprint easily searchable and optimised to make it as fast as possible.’ ‘What we have in mind with ToothPic is the ability to be able, with a suitably sized data centre, to process millions of pictures per second,’ said Prof. He launched a separate ERC-funded project, known as ToothPic, in late 2015, to look specifically at the camera identification issue. Professor Enrico Magli, of Politecnico di Torino, is using the compression technology of this project, which was funded with a grant from the EU’s European Research Council (ERC), to streamline the identification of the cameras behind online images. For one, the extracted fingerprints dwarf their original images, hindering large-scale sensor pattern noise applications due to the brute computational force and extensive storage space required.īut a team of Italian researchers working on a project named CRISP, developing technology for compressing signals and images in big data, stumbled onto a potential solution. The research enabling the extraction of these weak, but universal traces already emerged in 2006, but several problems remained to be solved. The pixel imperfections of a camera’s sensor leave a unique fingerprint on the images it creates, known as sensor pattern noise. In an ideal world, photographs of a pure blue sky taken by two identical cameras would be indistinguishable. Even with the most advanced manufacturing methods, every camera sensor is not created equal.įor example, a 10 megapixel camera sensor has an array of 10 million pixels - tiny cavities which trap light, turning it into a digital signal - and each of these can vary in how it measures light.
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