Authenticity identification method of cigarette box trademark paper based on improved frequency domain channel attention mechanism

This study introduces IFcaNet, an advanced method for authenticating cigarette packaging. It segments the image into three sub-images, employs FcaNet for detailed feature extraction, and leverages self-attention to boost feature representation. By integrating salient and average features through max and average pooling, the model gains comprehensive image insights. The concatenated features are then classified using a fully connected layer. Experimental results show that IFcaNet has superior classification accuracy and generalization over existing models, highlighting its effectiveness in counterfeit detection.