Aiming at the problem that the existing speaker verification systems for anti-spoofing， with convolutional model as main part， cannot capture global feature dependency well， an speaker verification system utilizing global-local feature dependency for anti-spoofing was proposed. Firstly， for the speech spoofing detection module， two filter combination ways were designed to filter the original speech， and sample augmentation was achieved by masking the frequency sub-bands. Secondly， a multi-dimensional global attention mechanism was proposed， where the global dependencies of each dimension were obtained by pooling the channel dimension， frequency dimension， and time dimension， respectively， and the global information was fused with the original features by weighting. Finally， for the speaker verification part， a Statistical Pyramid Dense Time Delay Neural Network （SPD-TDNN） was introduced to compute the standard deviation of the features and add the global information while obtaining the multi-scale time-frequency features. Experimental results show that on ASVspoof2019 dataset， the proposed speech spoofing detection system reduces the Equal Error Rate （EER） by 65.4% compared to Audio Anti-Spoofing using Integrated Spectro-Temporal graph attention network （AASIST） model， the proposed speaker verification system for anti-spoofing reduces the spoofing-aware speaker verification EER by 97.8% compared to the separate pyramid pooling speaker verification system. The above verifies that the proposed two modules achieve better classification results with the help of global feature dependency.