Satellite Edge Computing （SEC） improves computational efficiency for remote users by offloading tasks to satellite servers. However， SEC system performance may be affected by wireless transmission quality， since there is signal interference among User Equipments （UEs）. To this end， we proposed a User-Centric SEC （UCSEC） architecture to allocate Access Point （AP） clusters dynamically according to the real-time location of users and network conditions. To handle dynamic task offloading in UCSEC， we designed a Master-Slave multi-agent Proximal Policy Optimization with Gated Recurrent Unit （GRU） and Multi-head Attention mechanism （MSPPO_GMA） algorithm. The algorithm adopted a centralized control-distributed execution framework. The master agent， deployed on the SEC server， monitored global resource constraints and coordinated slave agents， while slave agents deployed on the UE executed task offloading and resource allocation according to network states. In MSPPO_GMA algorithm， GRU captured temporal correlations of network states， while multi-head attention focused on critical information of task offloading and resource allocation. Experimental results demonstrate that compared with MAPPO， MATD3 and other algorithms， MSPPO_GMA algorithm reduces task completion latency by 3.7% to 13.6% and UE energy consumption by 6.5% to 13.2%.

The existing deepfake detection methods mainly model on the basis of pixel-level clues of images， and seldom consider the impact of the synthesis process on the forged images. Although good detection results are achieved， it is difficult to explain the detection process. Therefore， a multi-modal physical prior feature fusion-based explainable detection method for deepfakes was proposed. First， optical flow features， illumination features， edge features and DCT（Discrete Cosine Transform） 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， so as to obtain multi-modal physical prior features with explainability. Second， a multi-modal mixture of experts network was proposed to construct expert sub-networks for different modalities， and after cross-modal attention weighting， the sub-networks were fused through a gated unit and input into the discriminative network for classification. Third， the SIAM （Spatial Intersection Attention Module） was introduced into the discriminative network， and the fully connected structure was replaced by the KAN （Kolmogorov-Arnold Network） structure. Finally， 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， thereby constructing a pre-feature-post-explanation explainable analysis framework to provide pixel-level explanations for model inference and prediction. Experimental results show that compared with algorithms such as CORE（COnsistent REpresentation learning）， SRM（Rich Models for Steganalysis）， and UCF（Uncovering Common Features）， the proposed method achieves the best performance on AUC and accuracy， with an accuracy range of 97.35% to 98.75% and an average accuracy of 98.22% on FaceForensics++ dataset， and the model’s interpretability also is improved significantly.