The field of synthetic image detection and generative AI is rapidly evolving, with a focus on developing more effective and robust methods for detecting and preventing fake content. Recent research has explored the use of dual-routing mixture of discriminative experts, causal inference, and multimodal large language models to improve detection accuracy and generalization. Notably, the development of novel frameworks and datasets, such as those for medical forensics and brand-obsessed text-to-image models, has enabled more accurate and equitable content generation.

Some noteworthy papers in this area include: TrueMoE, which proposes a novel dual-routing Mixture-of-Discriminative-Experts framework for synthetic image detection. Toward Medical Deepfake Detection, which introduces a large-scale medical forensics dataset and a novel Dual-Stage Knowledge Infusing detector for AI-generated medical images. CIDER, which proposes a model-agnostic framework to mitigate brand bias in text-to-image models through prompt refinement. ThinkFake, which leverages a Multimodal Large Language Model equipped with a forgery reasoning prompt for AI-generated image detection.