In a biometric system, each biometric sample or template is typically
associated with a single identity. However, recent research has demonstrated
the possibility of generating "morph" biometric samples that can successfully
match more than a single identity. Morph attacks are now recognized as a
potential security threat to biometric systems. However, most morph attacks
have been studied on biometric modalities operating in the image domain, such
as face, fingerprint, and iris. In this preliminary work, we introduce Voice
Identity Morphing (VIM) - a voice-based morph attack that can synthesize speech
samples that impersonate the voice characteristics of a pair of individuals.
Our experiments evaluate the vulnerabilities of two popular speaker recognition
systems, ECAPA-TDNN and x-vector, to VIM, with a success rate (MMPMR) of over
80% at a false match rate of 1% on the Librispeech dataset.

通过语音创作来模仿两个个体的声音特征，对两种流行的说话者识别系统 ECAPA-TDNN 和 x-vector 进行实验评估，并获取了在 Librispeech 数据集上，误配率为 1% 时的 80% 以上成功率。

语音转换：一声音中的两个身份

Voice Morphing: Two Identities in One Voice

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。