Approximating Optimal Morphing Attacks using Template Inversion
Laurent Colbois Hatef Otroshi Shahreza Sébastien Marcel
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.