The existing Generative Adversarial Network (GAN) synthesized face detection method has misjudgment of real faces with angles and occlusion, therefor a GAN-synthesized face detection method based on Deep Alignment Network (DAN) was proposed. Firstly, a facial landmark extraction network was designed based on DAN to extract the locations of facial landmarks of genuinus and synthesized faces. Then, in order to reduce the redundant information and feature dimensionality, each group of landmarks was mapped to the three-dimensional space by using the Principal Component Analysis (PCA) method. Finally, the features were classified by using 5-fold cross-validation of Support Vector Machine and the accuracy was calculated. Experimental results show that the proposed method improves the face dissonance caused by location errors by improving the accuracy of facial landmark location, which reduces the misjudgment rate of real faces. Compared with VGG19, XceptionNet and Dlib-SVM methods, this proposed method has the Area Under Receiver Operating Characteristic curve (AUC) increased by 4.48 to 32.96 percentage points and Average Precision (AP) increased by 4.26 to 33.12 percentage points on frontal faces; and has the AUC increased by 10.56 to 30.75 percentage points and AP increased by 7.42 to 42.45 percentage points on faces with angles and occlusion.