Specialist-Generalist Fusion with Outcome-Supervised Rationales for Deepfake Detection
Generalizable deepfake detection requires complementary forensic and semantic visual evidence. Specialist encoders capture subtle manipulation traces but can overfit to source-specific statistics, whereas MLLMs provide broader visual-semantic representations but may overlook fine forensic artifacts. We propose a two-stage detector in which an MLLM directly fuses patch-level features from a frozen forensic encoder with those from its native vision encoder and produces the authenticity decision itself. This specialist--generalist alignment provides the main cross-domain performance gain. We subsequently introduce outcome-supervised rationale tuning. The model generates a free-form visual rationale before its decision and is optimized using only the binary authenticity label and a format constraint, without task-specific rationale annotations. Rationale generation is optional at inference, so the tuned model can still return a direct binary score. On DF40, specialist--generalist alignment improves average cross-domain AUC from $89.85$ to $93.32\pm0.44$. Across six paired runs, rationale tuning obtains $93.50\pm0.42$ and improves five of six paired models; the mean difference is small and not statistically conclusive. Results on SID-Set and legacy benchmarks, together with fusion, output-order, and continued-training controls, demonstrate the value of complementary visual representations and show that label-only rationale tuning can add an optional explanation mode while approximately preserving direct detection performance.