Face Recognition Systems (FRS) are vulnerable to morph attacks. A face morph
is created by combining multiple identities with the intention to fool FRS and
making it match the morph with multiple identities. Current Morph Attack
Detection (MAD) can detect the morph but are unable to recover the identities
used to create the morph with satisfactory outcomes. Existing work in
de-morphing is mostly reference-based, i.e. they require the availability of
one identity to recover the other. Sudipta et al. \cite{ref9} proposed a
reference-free de-morphing technique but the visual realism of outputs produced
were feeble. In this work, we propose SDeMorph (Stably Diffused De-morpher), a
novel de-morphing method that is reference-free and recovers the identities of
bona fides. Our method produces feature-rich outputs that are of significantly
high quality in terms of definition and facial fidelity. Our method utilizes
Denoising Diffusion Probabilistic Models (DDPM) by destroying the input morphed
signal and then reconstructing it back using a branched-UNet. Experiments on
ASML, FRLL-FaceMorph, FRLL-MorDIFF, and SMDD datasets support the effectiveness
of the proposed method.

提出了一种无参考的去形变方法 SDeMorph，利用 DDPM 和分支 UNet，实现了高质量的去形变，能够恢复出真实身份。

SDeMorph：单一变形拆除脸部变形的提升

SDeMorph: Towards Better Facial De-morphing from Single Morph

Face morphing attack is proved to be a serious threat to the existing face
recognition systems. Although a few face morphing detection methods have been
put forward, the face morphing accomplice's facial restoration remains a
challenging problem. In this paper, a face de-morphing generative adversarial
network (FD-GAN) is proposed to restore the accomplice's facial image. It
utilizes a symmetric dual network architecture and two levels of restoration
losses to separate the identity feature of the morphing accomplice. By
exploiting the captured facial image (containing the criminal's identity) from
the face recognition system and the morphed image stored in the e-passport
system (containing both criminal and accomplice's identities), the FD-GAN can
effectively restore the accomplice's facial image. Experimental results and
analysis demonstrate the effectiveness of the proposed scheme. It has great
potential to be implemented for detecting the face morphing accomplice in a
real identity verification scenario.

本文提出了一种面部去变形生成对抗网络 (FD-GAN)，用于恢复面部欺诈同伙的图像，该网络具有双重对称结构和两个级别的恢复损失，通过利用面部识别系统捕获的面部图像，以及存储在电子护照系统中的变形图像，FD-GAN 可以有效地恢复同伴的面部图像，实验结果和分析证明了该方案的有效性和实际应用的巨大潜力。