  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.
