摘要
为鉴别社交网络中人脸图像的真假,在ConvNeXt基础上提出一种针对生成式对抗网络(Generative Adversarial Networks,GAN)生成人脸图像的鉴别方法。该方法以ConvNeXt网络结构为主体,利用人脸图像的颜色特征和空间纹理特征,采用多颜色空间多通道组合输入(Multichannel Input,MCI),扩大网络的学习范围;同时引入通道注意力机制和空间注意力机制来凸显真假人脸图像在颜色分量和纹理特征上的差异,进而实现生成人脸图像和真实人脸图像的检测与识别。实验结果表明,使用改进后的ConvNeXt(Improved ConvNeXt,I-ConvNeXt)网络结构对GAN生成人脸图像的识别准确率达到了99.405%,与原ConvNeXt算法相比,平均准确率提高了1.455个百分点。该结果验证了所提方案的可行性、合理性。
In order to distinguish the authenticity of face images in social networks,a recognition method based on ConvNeXt for face image generated by Generative adversarial networks(GAN)is proposed.The ConvNeXt network structure is used as the main body,using the color features and spatial texture features of the face image,and multi-channel combination input(Multi⁃channel Input,MCI)with multi-color space is used to expand the learning range of the network,while channel attention mecha⁃nism and spatial attention mechanism are introduced to highlight the differences between real and fake face images in color com⁃ponents and spatial features,and then the detection and recognition of fake face images are achieved.The experimental results show that the recognition accuracy of face images generated by GAN with improved ConvNeXt(I-ConvNeXt)network structure reaches 99.405%,with an average accuracy improvement of 1.455 percentage points compared with the original ConvNeXt algo⁃rithm.The results validate the feasibility and reasonableness of the proposed scheme.
作者
肖梦思
吴建斌
涂雅蒙
袁林锋
XIAO Mengsi;WU Jianbin;TU Yameng;YUAN Linfeng(College of Physics Science and Technology,Central China Normal University,Wuhan 430079,China;Wuhan Maritime Communication Research Institute,Wuhan 430079,China)
出处
《计算机与现代化》
2024年第4期38-42,共5页
Computer and Modernization
基金
国家自然科学基金资助项目(U1736121)
中央高校基本科研业务费专项资金资助项目(CCNU22JC024)。
关键词
生成式对抗网络
注意力机制
颜色特征
生成人脸
多通道输入
generative adversarial network
attention mechanism
color features
generated face image
multi-channel input
