论文标题
融合多级纹理和剩余描述符，用于多级2D条形码rebroadcasting检测
Fusing Multiscale Texture and Residual Descriptors for Multilevel 2D Barcode Rebroadcasting Detection
论文作者
论文摘要
如今，2D条形码已被广泛用于广告，移动付款和产品身份验证。但是，在与产品身份验证有关的应用中，可以通过绕过身份验证方案的方式非法复制真实的2D条形码。在本文中，我们采用专有的2D条形码模式，并使用多媒体法医方法来分析副本（Rebroadcasting）攻击产生的扫描和打印物品。提出了一个多样化和互补的功能集，以量化非法复制过程中引入的条形码纹理失真。所提出的特征由全球和本地描述符组成，这些描述符分别表征了多尺度纹理外观和兴趣点分布。在各种情况下（例如跨数据集和跨大小）将所提出的描述符与一些现有的纹理描述符和基于深度学习的方法进行了比较。实验结果突出了现实世界中提出的方法的实用性。
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.
