Hybrid attention network with appraiser-guided loss for counterfeit luxury handbag detection

Abstract

Recently, convolutional neural networks have shown good performance in many counterfeit detection tasks. However, accurate counterfeit detection is still challenging due to the following three issues: (1) fine-grained classification, (2) class imbalance, and (3) high imitation samples. To address these issues, we propose a hybrid attention network (HANet) for counterfeit luxury handbag detection. In HANet, a hybrid attention module is first designed. Compared with existing methods that directly use classic CNNs for counterfeit detection, the HA module jointly uses a channel attention unit and a spatial attention unit to learn important information on both the channel and spatial dimensions. The HA modules can be easily integrated into the ResNet architecture to enhance the discriminative representation ability of CNNs, so as to help the network find subtle differences between the real and counterfeit products. In addition, an appraiser-guided loss is proposed to train HANet. Considering the factor of class imbalance and high imitation samples, the proposed loss gives the counterfeit class a higher weighting, and meanwhile gives the high imitation samples a much higher weighting. The proposed loss introduces the knowledge of appraisers, which allows HANet to not only treat real and counterfeit samples relatively fairly, but also pay more attention to the learning of difficult samples. To evaluate the performance of our method, we have constructed a well-benchmarked luxury handbag dataset. On this dataset, the performance of HANet, ResNet50, and the state-of-the-art attention methods is compared. The results demonstrate that HANet achieve superior performance against all its competitors.