Research on Adaptive Attention Dense Network Structure in Camera Source Recognition Method

To enhance the recognition accuracy of deep learning models in the domain of camera source recognition, we have developed an adaptive attention dense network structure and introduced an adaptive weighted attention method. The proposed network structure is composed of four key components: a preprocessing module, a dense connection module, an attention module, and a regularization module. Central to the attention mechanism, the adaptive weighted attention method utilizes an adaptive weight factor to optimize parameters dynamically, enabling the model to adapt to varying data characteristics. This approach enhances the model’s feature learning and representation capabilities. Extensive comparative and ablation experiments were conducted on two benchmark datasets. In the comparative experiments, our network was evaluated against three well-established networks. The experimental results demonstrated that our network’s recognition performance surpassed that of the other networks by at least 5.6% on one dataset and 10% on the other. And the ablation studies revealed that the proposed method improved recognition performance by at least 2.6% and 5.2% compared to other ablation methods across the two datasets.