  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.
