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

arXiv: https://arxiv.org/abs/2201.07583 · HTML: https://mlsanity.com/paper/2201.07583/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 41 references that are in the map.
- First posted on arXiv 19 Jan 2022.

## Builds on (most cited first, 3 of 3)

- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [Detection and Localization of Image Forgeries using Resampling Features and Deep Learning](https://mlsanity.com/paper/1707.00433/) (2017)
- [Learning deep features for source color laser printer identification based on cascaded learning](https://mlsanity.com/paper/1711.00207/) (2017)

## Abstract (arXiv)

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.