Automatic generation of morphed face images often produces ghosting artifacts due to poorly aligned structures in the input images. Manual processing can mitigate these artifacts. However, this is not feasible for the generation of large datasets, which are required for training and evaluating robust morphing attack detectors. In this paper, we propose a method for automatic prevention of ghosting artifacts based on a pixel-wise alignment during morph generation. We evaluate our proposed method on state-of-the-art detectors and show that our morphs are harder to detect, particularly, when combined with style-transfer-based improvement of low-level image characteristics. Furthermore, we show that our approach does not impair the biometric quality, which is essential for high quality morphs.

翻译：变形人脸图像的自动生成常因输入图像中的结构对齐不佳而产生重影伪影。人工处理可减轻这些伪影，但对于生成训练与评估鲁棒变形攻击检测器所需的大规模数据集而言并不可行。本文提出一种基于变形生成过程中逐像素对齐的自动重影伪影预防方法。我们在最先进的检测器上评估了所提方法，实验表明我们的变形图像更难以被检测到，尤其是当结合基于风格迁移的低级图像特征改进时。此外，我们证明该方法不会损害生物特征质量，而高质量变形必须保持这一特性。