Recent works have demonstrated the feasibility of inverting face recognition systems, enabling to recover convincing face images using only their embeddings. We leverage such template inversion models to develop a novel type ofdeep morphing attack based on inverting a theoretical optimal morph embedding, which is obtained as an average of the face embeddings of source images. We experiment with two variants of this approach: the first one exploits a fully self-contained embedding-to-image inversion model, while the second leverages the synthesis network of a pretrained StyleGAN network for increased morph realism. We generate morphing attacks from several source datasets and study the effectiveness of those attacks against several face recognition networks. We showcase that our method can compete with and regularly beat the previous state of the art for deep-learning based morph generation in terms of effectiveness, both in white-box and black-box attack scenarios, and is additionally much faster to run. We hope this might facilitate the development of large scale deep morph datasets for training detection models.

翻译：近期研究已证明，通过嵌入向量即可恢复逼真人脸图像的面部识别系统反演技术具有可行性。我们利用此类模板反演模型提出一种新型深度形态攻击方法——通过反演理论上的最优形态嵌入（该嵌入由源图像人脸嵌入的平均值获得）实现攻击。我们实验了该方法的两种变体：第一种采用完全自包含的嵌入到图像的反演模型，第二种则利用预训练StyleGAN网络的合成模块增强形态真实感。我们基于多个源数据集生成形态攻击，并针对多个面部识别网络研究这些攻击的有效性。结果表明，在白盒与黑盒攻击场景下，我们的方法在攻击效能上均可与现有最优深度形态生成方法竞争并常能超越，同时执行速度显著更快。我们期望这能促进大规模深度形态数据集的开发，用于训练检测模型。