Detecting Misinformation with Multimodal AI: Leveraging Vision and NLP for Fact-Checking

TheThe rapid dissemination of misinformation on internet media severely undermines public opinion and decision- making. This study introduces a multimodal artificial intelligence approach that integrates computer vision (CV) and natural language processing (NLP) to identify misinformation. This methodology integrates deep learning vision models such as ResNet for image validation with transformer-based natural language processing models like BERT, RoBERTa, and GPT for textual examination, in contrast to traditional single-modal approaches. A complex fusion method connects textual and visual modalities, allowing a complete fact-checking system. Extensive testing on benchmark datasets shows that our methodology is better than conventional ones in accuracy, robustness, and efficiency in spotting altered or deceptive material. The suggested model increases misinformation detection using improved cross-modal verification; furthermore, assessing model performance, the study shows BERT as the most successful, with 99% accuracy on the LIAR dataset.