Social media has become increasingly significant in modern society, but it has also turned into a breeding ground for the propagation of misleading information, potentially causing a detrimental impact on public opinion and daily life. Compared to pure text content, multmodal content significantly increases the visibility and share ability of posts. This has made the search for efficient modality representations and cross-modal information interaction methods a key focus in the field of multimodal fake news detection. To effectively address the critical challenge of accurately detecting fake news on social media, this paper proposes a fake news detection model based on cross-modal message aggregation and a gated fusion network (MAGF). MAGF first uses BERT to extract cumulative textual feature representations and word-level features, applies Faster Region-based Convolutional Neural Network (Faster R-CNN) to obtain image objects, and leverages ResNet-50 and Visual Geometry Group-19 (VGG-19) to obtain image region features and global features. The image region features and word-level text features are then projected into a low-dimensional space to calculate a text-image affinity matrix for cross-modal message aggregation. The gated fusion network combines text and image region features to obtain adaptively aggregated features. The interaction matrix is derived through an attention mechanism and further integrated with global image features using a co-attention mechanism to produce multimodal representations. Finally, these fused features are fed into a classifier for news categorization. Experiments were conducted on two public datasets, Twitter and Weibo. Results show that the proposed model achieves accuracy rates of 91.8% and 88.7% on the two datasets, respectively, significantly outperforming traditional unimodal and existing multimodal models.

In response to the challenges of generating Attribute-Based Access Control (ABAC) policies, this paper proposes a deep learning-based method to automatically generate ABAC policies from natural language documents. This method is aimed at organizations such as companies and schools that are transitioning from traditional access control models to the ABAC model. The manual retrieval and analysis involved in this transition are inefficient, prone to errors, and costly. Most organizations have high-level specifications defined for security policies that include a set of access control policies, which often exist in the form of natural language documents. Utilizing this rich source of information, our method effectively identifies and extracts the necessary attributes and rules for access control from natural language documents, thereby constructing and optimizing access control policies. This work transforms the problem of policy automation generation into two tasks: extraction of access control statements and mining of access control attributes. First, the Chat General Language Model (ChatGLM) is employed to extract access control-related statements from a wide range of natural language documents by constructing unique prompts and leveraging the model’s In-Context Learning to contextualize the statements. Then, the Iterated Dilated-Convolutions-Conditional Random Field (ID-CNN-CRF) model is used to annotate access control attributes within these extracted statements, including subject attributes, object attributes, and action attributes, thus reassembling new access control policies. Experimental results show that our method, compared to baseline methods, achieved the highest F1 score of 0.961, confirming the model’s effectiveness and accuracy.