CIFT: Donor-Grounded Relational Gap Learning for Source-Free Face Forgery Detection

Face-forgery detectors often generalize poorly across synthesis pipelines because visual artifacts change with the renderer. We study whether source information available during training can provide a more transferable supervisory signal without being required at deployment. We introduce CIFT (Consistency-Invariant Forensic Training), a privileged-learning framework that uses a donor reference only during training. For swaps, the reference is the identity donor; for reenactment, it is the driving/source reference and defines a weaker relational discrepancy rather than identity replacement. XID-Mamba separately encodes the observed and donor streams with bidirectional Mamba modeling and symmetric cross-attention, while Type-Conditioned IGS structures their relation through complementary directional and magnitude constraints. Auxiliary diffusion regularizes the stream representations. At inference, all donor-dependent relational and diffusion components are discarded, leaving a single-image source-free detector. Trained only on FaceForensics++ (c23), CIFT generalizes strongly across five held-out benchmarks, remains effective under an encoder- and diffusion-prior-aligned comparison with DiffusionFake, and degrades under donor-correspondence interventions that replace the true donor with non-corresponding alternatives.