Face morphing attacks pose a severe security threat to face recognition systems, enabling the morphed face image to be verified against multiple identities. To detect such manipulated images, the development of new face morphing methods becomes essential to increase the diversity of training datasets used for face morph detection. In this study, we present a representation-level face morphing approach, namely LADIMO, that performs morphing on two face recognition embeddings. Specifically, we train a Latent Diffusion Model to invert a biometric template - thus reconstructing the face image from an FRS latent representation. Our subsequent vulnerability analysis demonstrates the high morph attack potential in comparison to MIPGAN-II, an established GAN-based face morphing approach. Finally, we exploit the stochastic LADMIO model design in combination with our identity conditioning mechanism to create unlimited morphing attacks from a single face morph image pair. We show that each face morph variant has an individual attack success rate, enabling us to maximize the morph attack potential by applying a simple re-sampling strategy. Code and pre-trained models available here: https://github.com/dasec/LADIMO

翻译：人脸融合攻击对生物识别系统构成严重安全威胁，使得融合后的人脸图像可通过多个身份验证。为检测此类篡改图像，开发新型人脸融合方法对于提升人脸融合检测训练数据集的多样性至关重要。本研究提出一种表征层面的人脸融合方法LADIMO，该方法在两个面部识别嵌入向量上进行融合操作。具体而言，我们训练潜在扩散模型实现生物特征模板反演——即从人脸识别系统的潜在表征重建人脸图像。后续脆弱性分析表明，相较于成熟的基于生成对抗网络的MIPGAN-II融合方法，本方法具有更高的融合攻击潜力。最后，我们结合随机化LADIMO模型设计与身份条件机制，实现了单组人脸图像对的无限次融合攻击生成。实验证明每个融合变体具有独立的攻击成功率，通过简单的重采样策略即可最大化融合攻击潜力。代码与预训练模型详见：https://github.com/dasec/LADIMO