Coevolutionary design of a watermark embedding scheme and an extraction algorithm for detecting replicated two-dimensional barcodes

Ono, Satoshi; Maehara, Takeru; Minami, Kazunari

doi:10.1016/j.asoc.2015.11.001

Cited by 8 publications

(1 citation statement)

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“…In order to tackle such an issue, 2D barcode authentication techniques proposed in the literature have been divided into active and passive categories. Active techniques [10], [11] are based on specific printing materials that could not be replicated by forgers and are commonly very expensive. On the other This work was partially funded by the MSCA-EU project PrintOut (Grant #892757) and also by the National National Science Foundation of China (Grant #62072313).…”

Section: Introductionmentioning

confidence: 99%

Fusing Multiscale Texture and Residual Descriptors for Multilevel 2D Barcode Rebroadcasting Detection

Ferreira

Chen

Barni

2021

2021 IEEE International Workshop on Information Forensics and Security (WIFS)

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Nowadays, 2D barcodes have been widely used for advertisement, mobile payment, and product authentication. However, in applications related to product authentication, an authentic 2D barcode can be illegally copied and attached to a counterfeited product in such a way to bypass the authentication scheme. In this paper, we employ a proprietary 2D barcode pattern and use multimedia forensics methods to analyse the scanning and printing artefacts resulting from the copy (rebroadcasting) attack. A diverse and complementary feature set is proposed to quantify the barcode texture distortions introduced during the illegal copying process. The proposed features are composed of global and local descriptors, which characterize the multi-scale texture appearance and the points of interest distribution, respectively. The proposed descriptors are compared against some existing texture descriptors and deep learning-based approaches under various scenarios, such as cross-datasets and cross-size. Experimental results highlight the practicality of the proposed method in real-world settings.

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Section: Introductionmentioning

confidence: 99%