论文标题
DMF-NET：双支车型多尺度功能融合网络，用于复制伪造识别抗征用QR码
DMF-Net: Dual-Branch Multi-Scale Feature Fusion Network for copy forgery identification of anti-counterfeiting QR code
论文作者
论文摘要
反征用QR码在人们的工作和生活中广泛使用，尤其是在产品包装中。但是，反征用QR码有被复制和锻造的风险。实际上，复制通常是基于真正的反相关QR码，但是复印机的品牌和模型是多种多样的，并且很难确定哪个单独的复印机来自哪种伪造的反相关代码。为了应对上述问题，本文提出了一种基于深度学习的伪造QR码的伪造方法。我们首先分析了反爆炸QR码的生产原理，并将复制伪造的识别转换为设备类别的取证，然后提出了双分支多尺度功能融合网络。在网络设计期间，我们对数据预处理层，单分支设计等进行了详细的分析，结合了实验，确定了双分支多尺度特征融合网络的特定结构。实验结果表明，所提出的方法已经达到了复制伪造识别的高精度，这超过了图像取证领域中当前的一系列方法。
Anti-counterfeiting QR codes are widely used in people's work and life, especially in product packaging. However, the anti-counterfeiting QR code has the risk of being copied and forged in the circulation process. In reality, copying is usually based on genuine anti-counterfeiting QR codes, but the brands and models of copiers are diverse, and it is extremely difficult to determine which individual copier the forged anti-counterfeiting code come from. In response to the above problems, this paper proposes a method for copy forgery identification of anti-counterfeiting QR code based on deep learning. We first analyze the production principle of anti-counterfeiting QR code, and convert the identification of copy forgery to device category forensics, and then a Dual-Branch Multi-Scale Feature Fusion network is proposed. During the design of the network, we conducted a detailed analysis of the data preprocessing layer, single-branch design, etc., combined with experiments, the specific structure of the dual-branch multi-scale feature fusion network is determined. The experimental results show that the proposed method has achieved a high accuracy of copy forgery identification, which exceeds the current series of methods in the field of image forensics.
