Generalizing Face Forgery Detection by Suppressed Texture Network With Two-Branch Convolution

With the development of Internet technology, deepfake (DF) videos can spread rapidly through online platforms, providing a new way of cyberbullying by generating nude pictures of female victims and using their faces to generate pornographic movies, which bring potential harm to individuals, society, and the country. Recently, there have been some really impressive results with DF detection models. These models have shown excellent outstanding performance when they are trained and tested using data from the same dataset. However, detecting DF remains difficult when the data comes from challenging datasets. To address this issue, this article aims to enhance the model's generalization by taking full advantage of the learning and representation capabilities of convolutional neural networks (CNNs) to adaptively suppress image texture information and catch deeper and more universal forgery features. Specifically, we introduce the texture suppression module (TSM) as a first step to suppress image content while simultaneously revealing the differences between authentic and tampered regions. Then, we carefully designed the cross stream interaction module (CSIM) and the cross stream mix block (CSMB) module to fully exploit the extracted forgery traces. Our proposed model has demonstrated superior generalization performance in extensive experiments.