基于空频联合卷积神经网络的GAN生成人脸检测

GAN-generated Face Detection Based on Space-Frequency Convolutional Neural Network

生成式对抗网络(GAN)的快速发展使其在图像生成领域取得了前所未有的成功。StyleGAN等新型GAN的出现使得生成的图像更真实且具有欺骗性,对国家安全、社会稳定和个人隐私都构成了较大威胁。文中提出了一种基于空频联合的双流卷积神经网络的检测模型。鉴于GAN图像在生成过程中因上采样操作在频谱上留下了清晰可辨的伪影,设计了可学习的频率域滤波核以及频率域网络来充分学习并提取频率域特征。为了减弱图像变换至频域过程中丢弃部分信息而带来的影响,同样设计了空间域网络来学习图像内容本身具有差异化的空间域特征,最终将两种特征融合来实现对GAN生成人脸图像的检测。在多个数据集上的实验结果表明,所提模型在高质量生成数据集上的检测精度及在跨数据集的泛化性上都优于现有算法,且对于JPEG压缩、随机剪裁、高斯模糊等图像变换具有更强的鲁棒性。不仅如此,所提方案在GAN生成的局部人脸数据集上也有不错表现,进一步证明了所提模型有着更好的通用性以及更加广泛的应用前景。

The rapid development of generative adversarial networks(GANs)has led to unprecedented success in the field of image generation.The emergence of new GANs such as StyleGAN makes the generated images more realistic and deceptive,posing a greater threat to national security,social stability,and personal privacy.In this paper,a detection algorithm based on a space-frequency joint two-stream convolutional neural network is proposed.Since GAN images will leave clearly discernible artifacts on the spectrum due to the up-sampling operation during the generation process,a learnable frequency-domain filter kernel and frequency domain network are designed to fully learn and extract frequency-domain features.In order to reduce the influence of the information discarded from the image transformation to the frequency domain,a spatial domain network is also designed to learn that the image content itself has differentiated spatial domain features.Finally,the two features are fused to detect the face image generated by GAN.Experimental results on multiple datasets show that the proposed model outperforms existing algorithms in detection accuracy on high-quality generated datasets and generalization across datasets.And for JPEG compression,random cropping,Gaussian blur,and other operations,this method has stronger robustness.In addition,the proposed method also performs well on the local face dataset generated by GAN,which further proves that this model has better generality and wider application prospects.