Introduction: Fake news spread in various areas has a major negative impact on social life. Meanwhile, fake news with text and visual content is more compelling than text-only content and quickly spreads across social media. Therefore, detecting fake news is a pressing task for the current society.Methods: Concern the problem of extracting insufficient features, and the inability to merge multi-modality features effectively in detecting fake news. In this article, we propose a method for detecting fake news by fusing text and visual data. Firstly, we use two-branch to learn hidden layer information of modality to obtain more helpful features. Then we proposed a multimodal bilinear pooling mechanism to better merge textual and visual features and an attention mechanism to capture multimodal internal relationships for the detection of fake news.Results and discussion: The experimental results demonstrated that our methodology outperformed the current state-of-the-art methodology on publicly accessible Weibo and Twitter datasets.

Semantic segmentation in LiDAR point clouds has become an important research topic for autonomous driving systems. This paper proposes a dynamic graph convolution neural network for LiDAR point cloud semantic segmentation using a polar bird’s-eye view, referred to as DGPolarNet. LiDAR point clouds are converted to polar coordinates, which are rasterized into regular grids. The points mapped onto each grid distribute evenly to solve the problem of the sparse distribution and uneven density of LiDAR point clouds. In DGPolarNet, a dynamic feature extraction module is designed to generate edge features of perceptual points of interest sampled by the farthest point sampling and K-nearest neighbor methods. By embedding edge features with the original point cloud, local features are obtained and input into PointNet to quantize the points and predict semantic segmentation results. The system was tested on the Semantic KITTI dataset, and the segmentation accuracy reached 56.5%

Anti-malware engines report malware labels to detail malice, typically including tags of family, behavior, and platform classes. This capability has been heavily used by the security community to annotate malware families and build reference datasets, which is referred to as crowdsourcing malware family annotation. However, how to associate tags with their corresponding classes in chaotic malware labels (extract class-determined tags) and how to infer ground truth for weakly-tagged samples that hold controversial tags remain open problems. In this paper, we present a novel annotation pipeline to advance further, which includes an incremental parsing scheme and a maximum likelihood estimation scheme. The incremental parsing scheme treats behavior and platform tags as locators and achieves incremental parsing by introducing and iterating the following two algorithms: location first search, which hits family tags using locators, and co-occurrence first search, which finds new locators by family tags. The maximum likelihood estimating scheme models an engine’s ability to identify different families as a confusion matrix and introduces an expectation-maximization algorithm to estimate the matrix, as well as the unknown truth of samples. Experiments across four benchmark datasets indicate that our pipeline outperforms existing work, improving label-level parsing accuracy by an average of 29%, and improving inferring accuracy on weakly-tagged samples by an average of 9%. Our pipeline decouples parsing and inferring, which would pave the way for research on crowdsourcing malware family annotation.

Generating synthetic content through Generative AI (GAI) presents considerable hurdles for current fake news detection methodologies. Many existing detection approaches concentrate on feature-based multi-modal fusion, neglecting semantic relationships such as correlations and diversities. In this study, we introduce an innovative cross-modal matching-driven approach to reconcile semantic relevance (text-image consistency) and semantic gap (text-image heterogeneity) in multimodal fake news detection. Unlike the conventional paradigm of multi-modal fusion followed by detection, our approach integrates textual modality, visual modality (images), and text embedded within images (auxiliary modality) to construct an end-to-end framework. This framework considers the relevance of contents across different modalities while simultaneously addressing the gap in structures, achieving a delicate balance between consistency and heterogeneity. Consistency is fostered by evaluating inter-modality correlation via pairwise-similarity scores, while heterogeneity is addressed by employing cross-attention mechanisms to account for inter-modality diversity. To achieve equilibrium between consistency and heterogeneity, we employ attention-guided enhanced modality interaction and similarity-based dynamic weight assignment to establish robust frameworks. Comparative experiments conducted on the Chinese Weibo dataset and the English Twitter dataset demonstrate the effectiveness of our approach, surpassing the state-of-the-art by 7% to 13%.