# Research on the Face Forgery Detection Model Based on Adversarial Training and Disentanglement
###### Abstract
Secondly, the reliance on data samples with limited diversity causes the model to learn specific face forgery features and techniques, inhibiting its ability to adapt to variations in different forgery types. To address these significant challenges, it is necessary to develop methods that can operate robustly across diverse forgery types and datasets, while also being easily disentangled. Therefore, this paper proposes a new approach to enhance the generalization of face forgery detection models. By strategically constructing diverse face forgery images data and employing disentanglement techniques, our model reduces the interference caused by specific forgery-type features and does not rely on a narrow set of forgery examples. Take, for example, Figure 1b, which combines feature disentanglement and adversarial training. This framework not only produces a wealth of forged samples but also, to some extent, reduces the model's excessive attention on irrelevant forgery features. Specifically, this paper incorporates an adversarial training strategy into the model. By using image synthesis techniques, skillfully simulates forgery effects in critical facial areas such as the eyes, nose, and mouth, thereby expanding the diversity of samples in the original dataset. This proactive approach ensures that the model is not limited by the diversity of data it has been exposed to. Building on this, the paper utilizes a framework to learn disentangled representations of forgery-related features and forgery-type features, respectively. We give supervised labels to forgery-related features and forgery-type features, and extract features using a network with the same structure but no shared parameters. Different from previous complex works [8; 13; 14], we design a distribution classifier to disentangle forgery features and forgery type features from the perspective of mutual information, and design the corresponding classification loss. In addition, we also design the adversarial loss to minimize the mutual information between features to further enhance the disentangling effect and feature independence. By focusing on these two distinct aspects during the training process, the model is guided to extract pertinent information related to forgeries more effectively, thereby enhancing its ability to generalize and improving the overall effectiveness of feature disentanglement. To validate the proposed framework, comprehensive evaluation experiments and ablation studies were conducted, confirming that this approach significantly improves the performance of face forgery detection. The main contributions of this work are summarized as follows:  
1. We identify a new problem in the realm of feature disentanglement in face forgery detection. Some research efforts are confined to specific datasets, leading to the extraction of forgery features that are only representative of the characteristics inherent to those datasets. This limitation may consequently hinder the models' ability to generalize when applied to unseen datasets. 2. We propose a new framework for face forgery detection based on adversarial training and disentanglement. This framework employs adversarial training and targeted techniques to produce samples resembling forgeries, thereby enriching the dataset. Subsequently, we design a simple feature disentanglement module, accompanied by a tailored mutual information loss function. This function is crafted to effectively distinguish between forgery features and forgery type features across the diversified dataset. This dual strategy significantly enhances the overall effectiveness of the model.