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

Though recent studies have made significant progress in morph attack
detection by virtue of deep neural networks, they often fail to generalize well
to unseen morph attacks. With numerous morph attacks emerging frequently,
generalizable morph attack detection has gained significant attention. This
paper focuses on enhancing the generalization capability of morph attack
detection from the perspective of consistency regularization. Consistency
regularization operates under the premise that generalizable morph attack
detection should output consistent predictions irrespective of the possible
variations that may occur in the input space. In this work, to reach this
objective, two simple yet effective morph-wise augmentations are proposed to
explore a wide space of realistic morph transformations in our consistency
regularization. Then, the model is regularized to learn consistently at the
logit as well as embedding levels across a wide range of morph-wise augmented
images. The proposed consistency regularization aligns the abstraction in the
hidden layers of our model across the morph attack images which are generated
from diverse domains in the wild. Experimental results demonstrate the superior
generalization and robustness performance of our proposed method compared to
the state-of-the-art studies.

从一致性正则化的角度提高形态攻击检测的泛化能力，通过两种简单但有效的形态增强方式，在各种领域生成的形态攻击图片上对模型进行逻辑和嵌入层级上的一致性正则化，实验结果表明与现有研究相比，我们提出的方法具有更高的泛化性能和鲁棒性。