  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.
