基于四元数的强泛化性GAN生成人脸检测算法

GAN-Generated Face Detection with Strong Generalization Ability Based on Quaternions

基于生成式对抗网络(generative adversarial network,GAN)模型生成的逼真人脸图像给司法、刑侦、名誉保护等带来了挑战.因此,基于四元数提出一种具有强泛化性的GAN生成人脸检测算法,其由GAN噪声指纹提取模块与分类模块组成.GAN噪声指纹提取模块采用孪生四元数U-Net提取噪声指纹特征;分类模块基于提取的噪声指纹特征采用四元数ResNet区分真实人脸和生成人脸;使用基于距离的逻辑回归损失函数和交叉熵损失函数对参数进行寻优.采用公开的自然人脸数据集CelebA进行实验,基于其利用多种GAN模型得到不同生成人脸数据集.4组消融实验验证了该算法在4个方面的改进的有效性.一种GAN生成人脸数据训练多种GAN测试的结果以及鲁棒性实验结果表明,所提算法比对比算法具有更强的泛化性以及较好的抵抗JPEG攻击的鲁棒性.

Models based on generative adversarial network(GAN)can generate very realistic face images,which bring additional challenges to justice,criminal investigation and reputation protection,etc.Therefore,a GAN-generated face detection algorithm having a strong generalization ability is proposed by using quaternions.The algorithm consists of a GAN noise fingerprint extraction module and a classification module.The former module uses Siamese quaternion U-Net to extract fingerprint features.The latter module adopts quaternion ResNet to distinguish the natural face from the GAN-generated face based on the extracted fingerprint features.In addition,the distance based logistic loss and cross entropy loss are used to optimize parameters.Experiments are based on a public natural face dataset CelebA,and some generated face datasets that generated by various GANs trained on the CelebA.Four groups of ablation experiments verify the improvements of the proposed algorithm in four aspects.The experiments,in which training data generated by only one kind of GAN but testing data by multiple GANs,show that the proposed algorithm has stronger generalization ability than the existing algorithms.Robustness experiments show that the proposed algorithm is also robust against JPEG attacks.