Micro-Grained Feature Enhanced Network for High-Accuracy Counterfeit Identification in Luxury Products

Significant progress has been made in luxury goods authentication using fine-grained image classification to exploit morphological differences in LOGO authentication points. However, with the advancement of luxury counterfeiting techniques, traditional fine-grained methods based on LOGO morphological differences face severe challenges: the micro-level differences between high-quality counterfeits and genuine products have approached the limit of human visual resolution. To address this, this paper proposes an authentication approach focusing on the micro-grained differences between authentic and counterfeit luxury goods. The key challenge lies in that existing deep learning models are distracted by macro signals, making it difficult to represent and extract micro-grained information. To overcome this, we innovatively design a Micro-Grained Feature Enhancement Module and a Multi-Scale Feature Learning Network:The former introduces a background replacement mechanism that generates diverse backgrounds for semantically identical foregrounds, preventing the model from relying on macro background information to establish decision boundaries. This forces the model to focus on micro-grained differences in the foreground.The latter proposes a multi-scale feature capture module that establishes an adaptive key region localization and multi-scale feature fusion mechanism, combined with a weighted voting strategy to enhance classification robustness. Experimental results on two luxury goods datasets demonstrate that the diverse background mechanism and multi-scale feature fusion significantly enhance the representation of micro-grained features. Visualization results effectively show that the model's attention shifts from distracting strong-signal background regions to enhanced micro-grained foreground regions, significantly improving its ability to distinguish high-precision counterfeits and greatly enhancing the credibility of its decisions.