Towards Generalizable Deepfake Detection: An Inconsistency-Aware Vision–Language Model Tuning Approach

Deepfakes generated by advanced AI models pose growing challenges to digital media authenticity. Large Vision–Language Models (VLMs) have recently been explored for image forensics due to their multimodal representation ability. However, many existing VLM-based deepfake detection methods keep the visual encoder fixed to preserve pre-trained knowledge, which may limit the model’s sensitivity to low-level inconsistency artifacts that are important for deepfake detection. To address this issue, we propose IncoTune, an inconsistency-aware tuning framework that introduces trainable vision-side adaptation into the visual encoder and applies Directional Low-Rank Adaptation (DoRA) to selected linear projection layers in both the visual encoder and the language model. We further report an empirical observation in the ablation study: under the evaluated adapter configuration, replacing LoRA with DoRA in the frozen-vision setting does not improve the average AUC, whereas DoRA provides additional gains when combined with vision-side adaptation. Experimental results on FaceForensics++, DFD, Celeb-DF, DFDC, and DFDCP show that IncoTune improves cross-dataset frame-level detection performance over the frozen-vision baseline and achieves competitive performance among representative frame-level methods, while updating only 27.0M adapter parameters during training. Robustness experiments further evaluate the model behavior under common image degradations.