Multimodal fake information increasingly threatens the Web ecosystem's trustworthiness and security, making improving detection accuracy a critical scientific challenge. The limited information interaction in traditional multimodal fake news detection methods fails to leverage semantic knowledge to model complex cross-modal forgery patterns and global structural anomalies, restricting the model's capability. To address the issues, this paper proposes a multimodal fake news detection method, SVPF-Net, that centers on semantic-driven visual enhancement and knowledge-aided modality-priority fusion. For visual representation optimization, we design a dual-feature extraction module and a dual-fusion enhancement module. A weighted fusion strategy is employed to construct a structured visual representation that integrates the semantics of local forgeries and global anomalies. Meanwhile, a cross-attention mechanism enables bidirectional alignment and interactive coupling between local and global image features, thereby achieving effective complementarity between local forgery cues and global anomaly patterns. For multimodal fusion, high-quality textual semantic features and visual representations are integrated via a modality-priority progressive fusion strategy that relies on cross-attention. The integration enables robust cross-modal semantic interaction and effectively enhances the efficiency of multimodal feature fusion. Comprehensive experiments validate the optimal performance of SVPF-Net and its ability to enhance interpretable semantics, providing valuable support for the practical application of reliable fake news detection.

Images playa vital role in modern communication, cultural exchange, and information dissemination. Document images, as a digital form of textual content, are widely used in government, finance, and e-commerce scenarios. However, tampered with document images has become increasingly common and is often exploited for identity fraud and financial scams, posing serious threats to platform credibility and public interests. Compared to natural images, document forgeries typically involve small, visually inconspicuous regions against uniform backgrounds, making RGB-based detection methods less effective. To address these challenges, we propose MFPD-Net (Multi-scale Frequency Progressive Detector), which integrates a Transformer-based frequency feature enhancement module combined with an adaptive feature fusion strategy. Additionally, a Progressive multi-scale feedback decoder is designed to refine mask generation and improve localization accuracy. Extensive experiments on multiple document tampered detection tasks demonstrate that our method outperforms existing approaches in both accuracy and robustness, showing strong practicality and generalization capabilities.