Objective Aiming at the problems of insufficient generalization and poor robustness of existing face forgery detection methods an improved face forgery detection method based on adversarial training ATNet is proposed. Methods ATNet expands the sample space by dynamically synthesizing adversarial forgery samples to enhance the model?? s ??sensitivity to forgery samples generated by different forgery algorithms and avoid the over-fitting of the model caused by over-sampling of training samples. By replacing the forgery regions marked by specific landmark points the model?? s ability to learn different forgery features is improved. A parallel learning strategy of high-frequency noise and low- frequency textures is adopted to fuse multi-level convolutional features which enables the model to capture more comprehensive forgery clues and more effectively identify forged images. Results With the effective generation of adversarial samples ATNet can learn more essential forgery features. Compared with current excellent methods such as Xception F3Net and Face X-ray ATNet has varying degrees of improvement in detection accuracy and generalization. The cross-dataset test results show that the model has excellent generalization performance. Visualization using the dimensionality reduction algorithm can intuitively determine ATNet ?? s ability to detect deep-forged images. Conclusion The face forgery detection method based on improved adversarial training can effectively improve the detection accuracy and generalization and strengthen the model?? s ??sensitivity to various forgery features. The experimental results on multiple datasets show that ATNet is simple and effective.