AI-generated misinformation (AIGM)

characterized by high generation efficiency

strong content fidelity

and rapid dissemination capabilities

poses a systemic threat to social trust

democratic processes

and public safety. To address this challenge

active defense strategies

such as content watermarking and model provenance authentication

are integrated. This research breaks through technical bottlenecks in Bayesian Neural Networks (BNNs) for uncertainty quantification and federated learning with dynamic updates

establishing a dynamic ethical alignment theory. However

it faces challenges including rapid generative technology evolution

multimodal detection complexity

and privacy protection. Continuous development of universal foundational detection models and secure hardware-enhanced watermarking technologies is required.