Abstract: The existing verification methods mainly model based on pixel-level clues of images, and seldom consider the impact of the synthesis process on the forged images. Even if good verification results are achieved, it is difficult to explain the verification process. Therefore, a multi-modal physical prior feature fusion-based explainable verification method for deep fakes was proposed. Firstly, optical flow features, illumination features, edge features and DCT features were used to describe the inter-frame motion differences in temporal videos, the illumination inconsistency in single-frame videos, and edge artifact information, respectively, to obtain multi-modal physical prior features with explainability. Secondly, a multi-modal hybrid expert network was proposed, which constructed expert sub-networks for different modalities, and after cross-modal attention weighting, they were fused through a gating unit and input into the discriminative network for classification. The SIAM attention mechanism was introduced into the discriminative network, and the fully connected structure was replaced by the KAN (Kolmogorov-Arnold Networks) structure. The multi-modal physical prior features were used to train different expert sub-networks respectively, and the Shapley value analysis of different input features was given, constructing a pre-feature-post-explanation explainable analysis framework to provide pixel-level explanations for model inference and prediction. The experimental results show that, compared with algorithms such as CORE, SRM, and UCF, the proposed algorithm achieves an accuracy range of 97.35% to 98.75% on the FaceForensics++ dataset, with an average accuracy of 98.22%., and the model's interpretability has also been significantly improved.