CGMamba: Intelligent Identification of Counterfeit Goods Based on State Space Models

Abstract

The global economy and society are seriously threatened by the pervasive spread of counterfeit goods. Their high level of simulation makes real and fake goods extremely similar in appearance and difficult to distinguish. The existing identification techniques mostly use CNNs and transformer architectures. However, CNNs have limitations in modeling long-range dependencies, leading to their limited classification performance, while vision transformers (ViTs), although excellent in modeling long-range dependencies, the quadratic computational complexity of their self-attention mechanism makes it difficult to be widely used in real-world scenarios with limited computational resources. According to recent research, long-range relationships can be accurately modeled using the state space model (SSM), which is represented by Mamba, while preserving linear computational complexity. Motivated by this, we proposed CGMamba, a SSM-based intelligent recognition model for counterfeit goods. Specifically, we constructed a novel hybrid basic block called global-local feature aggregation (GLFA). This block greatly enhances the feature extraction capability for counterfeit goods by deeply integrating the local feature extraction capability of the CNN and the global modeling capability of SSM. It is composed of three components: a local feature extractor, a global feature extractor, and an adaptive feature aggregation module (AFAM). In addition, to address the problem of lack of counterfeit goods image data, we constructed a large counterfeit goods dataset containing 101,480 images covering 104 categories for model training and evaluation. The experimental results showed that CGMamba achieved 90.99% Top 1 accuracy on the self-constructed dataset and 79.5% on the public dataset CNFOOD-241, which significantly outperforms the existing methods. The source code is available at https://github.com/wth1998/CGMamba.git.