Adaptive Forensic Feature Refinement via Intrinsic Importance Perception
The I2P framework improves synthetic image detection (SID) using visual foundation models by adaptively selecting the most discriminative layer for forgery cues and controlling parameter updates to protect pretrained knowledge. This unified approach enhances generalization across different data distributions while preserving the model's original structure, leading to stable and competitive performance on cross-distribution tasks.