With the rapid development of generative models and multimodal content editing technologies, the key challenge in synthetic image detection (SID) lies in cross-distribution generalization to unknown generation sources. Visual foundation models (VFM), which acquire rich visual priors through large scale image-text alignment pretraining, have become a promising approach to improving SID generalization. However, existing VFM-based methods remain coarse-grained in their adaptation strategies. They typically either directly use the final layer representations of VFM or simply fuse multi layer features, lacking explicit modeling of the optimal representational hierarchy for transferable forgery cues. Although fine-tuning VFM can enhance task adaptation, it may also damage the cross-modal pretrained structure that supports open-set generalization. To address this task specific tension, we reformulate VFM adaptation for SID as a joint optimization problem: it is necessary both to identify the critical representational layer that is more suitable for carrying forgery discriminative information and to constrain the disturbance caused by task knowledge injection to the pretrained structure. Based on this, we propose I2P, an SID framework centered on intrinsic importance perception. I2P first adaptively identifies the critical layer representations that are most discriminative for SID, and then constrains task-driven parameter updates within a low sensitivity parameter subspace, thereby improving task specificity while preserving the transferable structure of pretrained representations as much as possible. We conduct systematic experiments and ablation analyses on multiple cross-domain benchmarks, and the results show that I2P achieves stable and competitive generalization performance.

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