Project description DEENESFRITPL A new method to secure documents The EU has stressed the crucial importance of secure travel and identity documents. Improving security, however, is a challenge because document fraud – the falsification of documents and the manipulation of anti-forgery devices and techniques – is evolving rapidly. The use of more sophisticated document inspection systems is crucial. The EU-funded PRINTOUT project will develop a new method to fuse/ensemble machine learning approaches by considering several investigative scenarios and creating classifiers that reduce the risk of attacks. Specifically, the project proposes the use of open-set classifiers. The aim is to find solutions for several printed document forensics applications as regards their source attribution, forgery of documents and illegal copies detection. Show the project objective Hide the project objective Objective With the extensive range of document generation devices nowadays, the establishment of computational techniques to find manipulation, detect illegal copies and link documents to their source are useful because (i) finding manipulation can help to detect fake news and manipulated documents; (ii) exposing illegal copies can avoid frauds and copyright violation; and (iii) indicating the owner of an illegal document can provide strong arguments to the prosecution of a suspect. Different machine learning techniques have been proposed in the scientific literature to act in these problems, but many of them are limited as: (i) there is a lack of methodology, which may require different experts to solve different problems; (ii) the limited range of known elements being considered for multi-class classification problems such as source attribution, which do not consider unknown classes in a real-world testing; and (iii) they don’t consider adversarial attacks from an experienced forger. In this research project, we propose to address these problems on two fronts: resilient characterization and classification. In the characterization front, we intend to use multi-analysis approaches. Proposed by the candidate in his Ph.D. research, it is a methodology to fuse/ensemble machine learning approaches by considering several investigative scenarios, creating robust classifiers that minimize the risk of attacks. Additionally, we aim at proposing the use of open-set classifiers, which are trained to avoid misclassification of classes not included in the classifier training. We envision solutions to several printed document forensics applications with this setup: source attribution, forgery of documents and illegal copies detection. All the approaches we aim at creating in this project will be done in partnership with a document authentication company, which will provide real-world datasets and new applications. Fields of science natural sciencescomputer and information sciencesartificial intelligencemachine learning Programme(s) H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility Topic(s) MSCA-IF-2019 - Individual Fellowships Call for proposal H2020-MSCA-IF-2019 See other projects for this call Funding Scheme MSCA-IF-EF-ST - Standard EF Coordinator UNIVERSITA DEGLI STUDI DI SIENA Net EU contribution € 183 473,28 Address Via banchi di sotto 55 53100 Siena Italy See on map Region Centro (IT) Toscana Siena Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00
