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Misinformation is false information that spreads regardless of whether there is intent to mislead the public, consisting fake news, rumours, telecom fraud, etc. As online social media lacks serious verification of posts, and most netizens are unable to discriminate between fake and real news, misinformation has proliferated in recent years, affecting all aspects of individuals and society. Although there are several products powered by advanced AI algorithms and blockchain technologies to tackle the threat of misinformation, existing AI algorithms require a considerable amount of labeled data for model training. This is considered unrealistic in practice because collecting a massive volume of news and posts is cumbersome, and the data rely highly on past events, so they may not be able to generalize to recent news events. Increasing multimodal content (i.e. posts with images) make this task even more challenging. On the other hand, blockchain-based products require the additional cost of setting separate identification codes for each piece of misinformation.

Thus, we h propose a domain-robust multimodal misinformation detection system, called Defender, which comprises an AI algorithms bank, an AI models bank and an online detection system to help government, businesses and individuals create a better-informed world.

Owing to the effective inference of our proposed AI model, enhanced by transferring learning algorithms, our Defender system can provide real-time and more accurate detection for large-scale information on social media platforms without a huge volume of annotation for all relevant posts for model training.