Name
Abstract | ||
|---|---|---|
Copy sensitive graphical codes are used as anti-counterfeiting solution in packaging and document protection. Their security is funded on a design hard-to-predict after print and scan. In practice there exist different designs. Here random codes printed at the printer resolution are considered. We suggest an estimation of such codes by using neural networks, an in-trend approach which has however not been studied yet in the present context. In this paper, we test a state-of-the-art architecture efficient in the binarization of handwritten characters. The results show that such an approach can be successfully used by an attacker to provide a valid counterfeited code so fool an authentication system.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3335203.3335718 | IH&MMSec |
Keywords | Field | DocType |
copy-sensitive codes, authentication, estimation attack, neural networks for binarization, print-and-scan process | Architecture,Authentication,Authentication system,Computer science,Theoretical computer science,Artificial neural network,Computer engineering | Conference |
ISBN | Citations | PageRank |
978-1-4503-6821-6 | 1 | 0.37 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rohit Yadav | 1 | 1 | 0.37 |
| Iuliia Tkachenko | 2 | 5 | 3.79 |
| alain tremeau | 3 | 230 | 34.42 |
| Thierry Fournel | 4 | 6 | 1.86 |