See, Then Reason: Reflective On-Policy Self-Distillation for Deepfake Detection

Multimodal large language models (MLLMs) have enabled deepfake detectors to support forensic reasoning alongside binary real-or-fake predictions. However, it remains unclear whether current MLLMs can preserve and effectively exploit the visual information required for deepfake detection. Our analysis reveals that deepfake detection performance consistently degrades across successive language-model layers. Reasoning-based SFT also underperforms the strongest intermediate-layer probe and exhibits a larger real-fake accuracy gap, indicating that the detection capability available in intermediate visual representations is not fully preserved in the final prediction. To address these limitations, we propose **SeeTR (See, Then Reason)**. Instead of entangling visual judgment with reasoning, SeeTR grounds the initial judgment in the most discriminative intermediate representation. It then builds reflective reasoning on this strong visual detection capability to determine whether the judgment should be kept or revised. Building on this reflective structure, we introduce **Contrastive Teacher On-Policy Self-Distillation (CT-OPSD)** to refine the model's reflective behavior on its own rollouts. SeeTR forms contrastive teacher contexts conditioned on the ground-truth label and its opposite, using their token-level disagreement to strengthen the model's ability to keep correct initial judgments and revise incorrect ones. Under the cross-domain & forgery setting, the initial visual judgment alone already outperforms prior MLLM-based detectors, and SeeTR further improves this visual baseline through reflective reasoning, achieving the highest average balanced accuracy among the evaluated MLLM-based detectors.