GMD: A Multimodal Framework for AI-Generated Misinformation Detection

The sophistication of AI-generated misinformation has escalated markedly, with modern systems exploiting multimodal compositions—text, images, and videos—to bypass conventional detection methods reliant on Unimodal analysis. Contemporary detection frameworks must evolve to address these challenges by exploiting cross-modal synergies. This work introduces a bilingual multimodal detection architecture that combines deep feature extraction from visual and textual modalities. Specifically, ResNet-derived encoders process images, while language-specific transformers (Qwen for Chinese, BERT for English) generate contextual embeddings. Empirical validation on benchmark datasets (e.g., Weibo) demonstrates state-of-the-art performance, with 3.6% accuracy gains over existing methods. Ablation tests confirm the necessity of both synthetic augmentation and adaptive fusion, underscoring their roles in refining feature discriminability. These advancements highlight the framework’s capability to address evolving threats in AI-generated misinformation.