With the widespread proliferation of the Internet, the spread of fake news has accelerated significantly, evolving from single-text content to multimodal forms that include images and videos. The task of Multimodal Fake News Detection (MFND) takes both text and relevant images as input for fake news identification. However, issues such as image noise and inaccurate focus of visual features often lead to insufficient attention to critical information within images during multimodal fusion. To effectively address these challenges, we propose a covariance matrix-driven image channel allocation method. This method first expands the number of original channel maps, then evaluates the importance of image channels through the covariance matrix and assigns importance scores to the expanded channel maps, thereby redirecting the focus of visual features. Subsequently, we design a multimodal fusion strategy based on a multilayer co-attention mechanism to achieve dynamic fusion across modalities. Finally, a contrastive learning loss is introduced to enhance the alignment between textual and visual modalities. Extensive experiments demonstrate that our method achieves state-of-the-art performance on three public multimodal fake news detection benchmark datasets.