关键词: 神经网络, 深度学习, 伪造人脸, 特征提取, 伪造图像检测

Abstract: The fake images generated by deep generative models are becoming increasingly realistic, surpassing the human eye’s ability to detect them. These models have become new tools for illegal activities, such as fabricating lies and creating public opinion. Although current researchers have proposed many detection methods to detect fake images, their generalization ability is typically limited. To address this issue, we proposed a fake face detection method based on ConvNeXt. Firstly, we add a PSA(polarization selfattention) module after the second and third downsampling modules of ConvNeXt, enhancing the network’s spatial and channel attention performance. Secondly, a RIB(rich imformation block) is designed at the end of ConvNeXt to enrich the information learned by the network. The information is processed through this module before final classification. Furthermore, the loss function used in network training is a combination of CrossEntropy loss and KL(KullbackLeibler) divergence. Extensive experiments on the current mainstream fake face datasets demonstrate that our method surpasses all comparative methods in accuracy and generalization on the FF++ C23 dataset.

Key words: neural network, deep learning, fake face, feature extraction, fake image detection