Abstract

A face morph is created by combining two face images corresponding to two identities to produce a composite that successfully matches both the constituent identities. Reference-free (RF) demorphing reverses this process using only the morph image, without the need for additional reference images. Previous RF demorphing methods are overly constrained, as they rely on assumptions about the distributions of training and testing morphs such as the morphing technique used (e.g., landmark-based) and face image style (e.g., passport photos). In this paper, we introduce a novel diffusion-based approach, referred to as diffDeMorph, that effectively disentangles component images from a composite morph image with high visual fidelity. Our method is the first to generalize across morph techniques and face styles, beating the current state of the art by ≥59.46% under a common training protocol across all datasets tested. We train our method on morphs created using synthetically generated face images and test on real morphs, thereby enhancing the practicality of the technique. Experiments on six datasets and two face matchers establish the utility and efficacy of our method.

Coupled Forward Diffusion

Denoising

Figure 1:(Top): Coupled forward diffusion: We modify the standard diffusion process for coupled generation. Noise is added to the coupled image until it is degenerated into pure noise. (Bottom): Architecture of our morph guided denoiser. During inference, the morph is appended to the noisy output sample at timestep=t, providing the guidance for generation of the outputs. This process is repeated until demorphed images at timestep=0 are recovered.

Resources

Citation

If you use this work, please cite:

@INPROCEEDINGS{shukla2025diffdemorph,
  author={Shukla, Nitish and Ross, Arun},
  booktitle={IEEE International Conference on Image Processing (ICIP, 2025)}, 
  title={diffDemorph: Extending Reference-Free Demorphing to Unseen Faces}, 
}