PhysCorr: Reliability-Controlled Correction for Visible-Infrared Camouflaged Object Segmentation in Unseen Environments

Visible–infrared camouflaged object segmentation (VICOS) is vulnerable in unseen environments, where the two modalities often suffer asymmetric degradation. Existing fusion methods rely on statistical complementarity and may propagate corrupted modality cues, while visual structures can be overwritten by unconstrained physical priors. To address these limitations, we propose PhysCorr, a reliability-controlled physical correction framework that selectively refines rather than replaces visual evidence. Specifically, imaging conditions are factorized into structured degradation states and target-invariant properties. Shared hierarchical features establish a stable visual reference, while residual correction is generated under physical-state modulation. A reliability-conditioned intervention mechanism integrates cross-modal agreement, branch reliability, spatial uncertainty, and boundary evidence to determine when, where, in which direction, and to what extent correction is applied. Counterfactual invariant learning aligns target representations and invariant reasoning states between observed conditions and clean counterfactuals, suppressing environment-specific shortcuts. Consequently, stable predictions are preserved under conventional conditions, while physical correction is adaptively strengthened in adverse environments. Experiments under conventional and cross-environment settings show that PhysCorr improves source-domain segmentation accuracy and robustness to unseen asymmetric degradation.