Finding more specific subcategories within a larger category is the goal of fine-grained image classification (FGIC), and the key is to find local discriminative regions of visual features. Most existing methods use traditional convolutional operations to achieve fine-grained image classification. However, traditional convolution cannot extract multi-scale features of an image and existing methods are susceptible to interference from image background information. Therefore, to address the above problems, this paper proposes an FGIC model (Attention-PCNN) based on hybrid attention mechanism and pyramidal convolution. The model feeds the multi-scale features extracted by the pyramidal convolutional neural network into two branches capturing global and local information respectively. In particular, a hybrid attention mechanism is added to the branch capturing global information in order to reduce the interference of image background information and make the model pay more attention to the target region with fine-grained features. In addition, the mutual-channel loss (MC-LOSS) is introduced in the local information branch to capture fine-grained features. We evaluated the model on three publicly available datasets CUB-200-2011, Stanford Cars, FGVC-Aircraft, etc. Compared to the state-of-the-art methods, the results show that Attention-PCNN performs better.
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As one of the essential steps to secure government data sharing, Identity Authentication (IA) plays a vital role in the processing of large data. However, the centralized IA scheme based on a trusted third party presents problems of information leakage and single point of failure, and those related to key escrow. Therefore, herein, an effective IA model based on multiattribute centers is designed. First, a private key of each attribute of a data requester is generated by the attribute authorization center. After obtaining the private key of attribute, the data requester generates a personal private key. Second, a dynamic key generation algorithm is proposed, which combines blockchain and smart contracts to periodically update the key of a data requester to prevent theft by external attackers, ensure the traceability of IA, and reduce the risk of privacy leakage. Third, the combination of blockchain and interplanetary file systems is used to store attribute field information of the data requester to further reduce the cost of blockchain information storage and improve the effectiveness of information storage. Experimental results show that the proposed model ensures the privacy and security of identity information and outperforms similar authentication models in terms of computational and communication costs.