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Anti-counterfeiting QR codes are widely used in people's work and life,especially in product packaging. However, the anti-counterfeiting QR code hasthe risk of being copied and forged in the circulation process. In reality,copying is usually based on genuine anti-counterfeiting QR codes, but thebrands and models of copiers are diverse, and it is extremely difficult todetermine which individual copier the forged anti-counterfeiting code comefrom. In response to the above problems, this paper proposes a method for copyforgery identification of anti-counterfeiting QR code based on deep learning.We first analyze the production principle of anti-counterfeiting QR code, andconvert the identification of copy forgery to device category forensics, andthen a Dual-Branch Multi-Scale Feature Fusion network is proposed. During thedesign of the network, we conducted a detailed analysis of the datapreprocessing layer, single-branch design, etc., combined with experiments, thespecific structure of the dual-branch multi-scale feature fusion network isdetermined. The experimental results show that the proposed method has achieveda high accuracy of copy forgery identification, which exceeds the currentseries of methods in the field of image forensics.

DMF-Net: Dual-Branch Multi-Scale Feature Fusion Network for copy forgery identification of anti-counterfeiting QR code