Generative Adversarial Networks (GANs) have shown success in approximating complex distributions for synthetic image generation. However, current GAN-based methods for generating biometric images, such as iris, have certain limitations: (a) the synthetic images often closely resemble images in the training dataset; (b) the generated images lack diversity in terms of the number of unique identities represented in them; and (c) it is difficult to generate multiple images pertaining to the same identity. To overcome these issues, we propose iWarpGAN that disentangles identity and style in the context of the iris modality by using two transformation pathways: Identity Transformation Pathway to generate unique identities from the training set, and Style Transformation Pathway to extract the style code from a reference image and output an iris image using this style. By concatenating the transformed identity code and reference style code, iWarpGAN generates iris images with both inter- and intra-class variations. The efficacy of the proposed method in generating such iris DeepFakes is evaluated both qualitatively and quantitatively using ISO/IEC 29794-6 Standard Quality Metrics and the VeriEye iris matcher. Further, the utility of the synthetically generated images is demonstrated by improving the performance of deep learning based iris matchers that augment synthetic data with real data during the training process.

翻译：生成对抗网络（GANs）在逼近复杂分布以生成合成图像方面已展现出成功。然而，当前基于GAN的生物特征图像生成方法（如虹膜图像）存在一定局限性：（a）合成图像常与训练数据集中的图像高度相似；（b）生成图像在所含独特身份数量方面缺乏多样性；（c）难以生成属于同一身份的多幅图像。为解决上述问题，我们提出iWarpGAN，通过两种变换路径解耦虹膜模态中的身份与风格：身份变换路径用于从训练集生成独特身份，风格变换路径用于从参考图像提取风格编码并据此输出虹膜图像。通过拼接变换后的身份编码与参考风格编码，iWarpGAN可生成兼具类间与类内变异性的虹膜图像。本文利用ISO/IEC 29794-6标准质量指标与VeriEye虹膜匹配器，从定性与定量角度评估了该方法生成此类虹膜深度伪造图像的有效性。此外，通过将合成数据与真实数据共同用于训练过程中，基于深度学习的虹膜匹配器性能得到提升，从而证明了合成图像的实际应用价值。