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A neural network based counterfeit detection system to verify the authenticity of products

Project description

The first artificial intelligence capable of detecting counterfeit products

The trade in counterfeit and pirated goods is booming. It’s a major challenge in an innovation-driven global economy, threatening sustainable business models based on intellectual property and patenting. It is claimed that counterfeit products and in particular counterfeit medicine kill more people than malaria. In the EU, counterfeited and pirated products account for about 5 % of imports. The EU-funded Microguard project will bring to market a counterfeit detection system that requires only a mobile telephone’s camera. The system, which is the first of it's kind to be released, uses an artificial intelligence capable of analysing the microstructure of printed materials and spotting any tiny mistakes made by counterfeiters.

Objective

Counterfeiting is a crime, involving the manufacturing or distribution of goods under someone else's name, and without their permission. Counterfeit goods are generally made from cheap and lower quality component that put the health and safety of consumers at risk.
According to an International Chamber of Commerce the total value of counterfeit and pirated goods globally is around €1.77 trillion. Each year EU companies lose €83 billion in sales due to counterfeited products. Although the counterfeiting has been a problem for decades, there is no solution yet for it fighting at consumer level.
Cypheme introduces a counterfeit detection system that allows a consumer to determine if a product is an original branded product using only a cell phone camera. The system uses a micro structured varnish that can only be read with a neural network technology developed by Cypheme to authenticate the product. The system can be applied directly on the product, making it harder for counterfeiters to copy.

During the feasibility assessment, a minimum viable product will be defined, a go-to-market strategy and a supply chain will be established, as well as further development plan will be drafted. Within the overall innovation project, Cypheme aims to adapt the varnish application to metal, glass and plastic and upscale the neural recognition software; optimize the user interface for brand owners and consumers; perform a quality demonstration and validation of the system at different goods.

Fields of science (EuroSciVoc)

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

SME-1 - SME instrument phase 1

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) H2020-EIC-SMEInst-2018-2020

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Coordinator

CYPHEME
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 50 000,00
Address
RUE BARGUE 27
75015 Paris
France

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SME

The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.

Yes
Region
Ile-de-France Ile-de-France Paris
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Links
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 71 429,00
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