To address the issue of unauthorized forgery or tampering of facial images， an active defense method based on attention mask and feature extraction was proposed. This method was designed to take offensive measures to interfere with forgery models by adding adversarial examples into the image， so that the image was prevented forgery from the source and the visual quality of the protected image was enhanced. Firstly， an improved gradient descent method was employed to generate and add adversarial perturbations to the original image， resulting in the generation of a blurred false image after forgery processing the original image. At the same time， the attention mask was incorporated into the generator to enhance key feature channels， thereby reducing the influence of complex backgrounds and lighting. Additionally， the VGG16 pre-trained network was utilized to extract image features， thereby improving the visual quality of adversarial images at feature map level. Experimental results on CelebFaces Attributes （CelebA） dataset and Radboud Faces Database （RaFD） dataset show that， for StarGAN， the defense success rates of the proposed model are 99.80% and 99.63% respectively. Compared with the baseline method based on spread-spectrum adversarial attack， the proposed method has the visual quality of generated adversarial images improved by 30.86% and 26.63% respectively on Structure Similarity Index Measure （SSIM）， and the Peak Signal-to-Noise Ratio （PSNR） improved by 34.80% and 36.15% respectively. The above indicates that the proposed method defends against face image forgery effectively while enhancing the visual quality of adversarial images.