Learning Text-Conditioned State-Space Recurrence for Multimodal Medical Logical Deepfake Verification

Generative models can produce biomedical images that are physically plausible yet contradict their accompanying text. We call such inconsistent image–text pairs multimodal medical logical deepfakes and study their detection as a defensive consistency-verification task. Image-only detectors cannot assess agreement with an unseen caption, while global visual self-attention incurs quadratic token interactions. We propose the Multimodal Bilateral Consistency Network (MM-BCNet), whose Text-Conditioned Vision Mamba (TC-VMamba) generates the selective state-space parameters ${\Delta }_i$, ${\mathbf{B}}_i$, and ${\mathbf{C}}_i$ from each local visual token and a global text representation. This establishes direct text-to-parameter conditioning within the selective scan, enabling linear-complexity cross-modal recurrence across high-resolution visual sequences without token expansion. A single-query Semantic-Guided Affinity module supplies a spatial prior, and a Seeing Before Reasoning schedule learns visual forensic cues before multimodal fine-tuning. We construct a synthetic benchmark covering Semantic Misalignment and Visual Inpainting, with source-grouped splits and generation masks for edited-region localization, a task distinct from establishing semantic contradiction. Against baselines, MM-BCNet improves consistency classification, localization, and unseen-generator accuracy.