# Research on the Face Forgery Detection Model Based on Adversarial Training and Disentanglement
###### Abstract  
With the advancement of generative models, face forgeries are becoming increasingly realistic, making face forgery detection a hot topic in research. The primary challenge in face forgery detection is the inadequate generalization performance. Numerous studies have proposed solutions to this issue; however, some methods heavily rely on the overall feature space of training samples, interfering with the extraction of key features for detection. Additionally, some studies design disentangled frameworks that overlook data diversity, limiting their effectiveness in complex real-world scenarios. This paper presents a model framework based on adversarial training and disentanglement strategy. Adversarial training is employed to generate forged samples that imitate the face forgery process, specifically targeting certain facial areas to simulate face forgery effects, which enriches data diversity. Simultaneously, the feature disentanglement strategies are employed to focus the model on forgery features, with a mutual information loss function designed to obtain the disentanglement effect. Additionally, an adversarial loss based on mutual information is designed to further enhance the disentanglement effect. On the FaceForensics++ dataset, our method achieves an AUC of 96.75%. Simultaneously, it demonstrates outstanding performance in cross-method evaluations with an accuracy of 80.32%. In cross-dataset experiments, our method also exhibits excellent performance. f [supp]supp_[supp]_supp_[  
data augmentation as a strategy to address the generalization challenge. They empirically design data augmentation techniques to create new face forgery samples, with the goal of broadening the diversity of forgery samples within the dataset. However, a limitation of this approach is the inadequate diversity in the augmentation strategies employed, which fails to fully represent the complexity of facial forgeries encountered in real-life scenarios. Studies [11; 12] explore the intrinsic properties of face forgeries by utilizing detail differences or frequency features to achieve a more extensive feature representation. A downside is their vulnerability to processes, such as image compression, which hampers the model's ability to generalize. Although some studies are dedicated to addressing this issue, the majority of existing methods primarily focus on analyzing the overall feature space of images [8], which complicates the process for detectors in capturing the key features necessary to distinguish between real and fake images. To avoid the interference of overall features in face forgery detection, studies [8; 13; 14] investigate the use of feature disentanglement technology for face forgery detection. By disentangling complex features into content and forgery features from various perspectives, they not only improve the interpretability of these features but also extract general traits that can effectively identify forgeries. However, although disentanglement allows the model to concentrate on essential features, the feature extraction in [8; 13; 14] is conducted on specific datasets without considering the diversity of the data. This approach constrains their adaptability across various datasets, thereby limiting the models' generalization capabilities. Moreover, their disentangled methods are relatively complex. Based on the above three approaches to improve generalization performance, this paper identifies two main reasons for limited generalization capability: Firstly, most methods mostly consider the entire feature space, making detectors vulnerable to interference by irrelevant features (such as background and identity information), like Figure 1a. This vulnerability complicates the model's ability to utilize features that can accurately distinguish forgeries, leading to a decline in generalization performance.