Facial Demorphing from a Single Morph Using a Latent Conditional GAN

• URL: http://arxiv.org/abs/2507.18566v2
• Date: Mon, 28 Jul 2025 06:43:49 GMT
• Title: Facial Demorphing from a Single Morph Using a Latent Conditional GAN
• Authors: Nitish Shukla, Arun Ross,
• Abstract summary: The proposed method decomposes a morph in latent space allowing it to demorph images created from unseen morph techniques and face styles.<n>We train our method on morphs created from synthetic faces and test on morphs created from real faces using different morph techniques.
• Score: 10.902536447343465
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A morph is created by combining two (or more) face images from two (or more) identities to create a composite image that is highly similar to all constituent identities, allowing the forged morph to be biometrically associated with more than one individual. Morph Attack Detection (MAD) can be used to detect a morph, but does not reveal the constituent images. Demorphing - the process of deducing the constituent images - is thus vital to provide additional evidence about a morph. Existing demorphing methods suffer from the morph replication problem, where the outputs tend to look very similar to the morph itself, or assume that train and test morphs are generated using the same morph technique. The proposed method overcomes these issues. The method decomposes a morph in latent space allowing it to demorph images created from unseen morph techniques and face styles. We train our method on morphs created from synthetic faces and test on morphs created from real faces using different morph techniques. Our method outperforms existing methods by a considerable margin and produces high fidelity demorphed face images.

Related papers

• diffDemorph: Extending Reference-Free Demorphing to Unseen Faces [10.902536447343465]
  diffDeMorph effectively disentangles component images from a composite morph image with high visual fidelity.<n>We train our method on morphs created using synthetically generated face images and test on real morphs, thereby enhancing the practicality of the technique.
  arXiv  Detail & Related papers  (2025-05-20T15:48:27Z)
• dc-GAN: Dual-Conditioned GAN for Face Demorphing From a Single Morph [10.902536447343465]
  Face demorphing attempts to recover the original images constituting a facial morph.<n> dc-GAN (dual-conditioned GAN) is a novel demorphing method conditioned on the morph image as well as the embedding extracted from the image.<n>Our method overcomes the morph replication problem and produces high-fidelity reconstructions of the constituent images.
  arXiv  Detail & Related papers  (2024-11-20T19:24:30Z)
• SDeMorph: Towards Better Facial De-morphing from Single Morph [0.0]
  Face Recognition Systems (FRS) are vulnerable to morph attacks. Current Morph Attack Detection (MAD) can detect the morph but are unable to recover the identities used to create the morph. We propose SDeMorph, a novel de-morphing method that is reference-free and recovers the identities of bona fides.
  arXiv  Detail & Related papers  (2023-08-22T13:46:12Z)
• Single-Shot Implicit Morphable Faces with Consistent Texture Parameterization [91.52882218901627]
  We propose a novel method for constructing implicit 3D morphable face models that are both generalizable and intuitive for editing. Our method improves upon photo-realism, geometry, and expression accuracy compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-04T17:58:40Z)
• MorphGANFormer: Transformer-based Face Morphing and De-Morphing [55.211984079735196]
  StyleGAN-based approaches to face morphing are among the leading techniques. We propose a transformer-based alternative to face morphing and demonstrate its superiority to StyleGAN-based methods.
  arXiv  Detail & Related papers  (2023-02-18T19:09:11Z)
• Facial De-morphing: Extracting Component Faces from a Single Morph [12.346914707006773]
  morph attack detection strategies can detect morphs but cannot recover the images or identities used in creating them. We propose a novel de-morphing method that can recover images of both identities simultaneously from a single morphed face image.
  arXiv  Detail & Related papers  (2022-09-07T05:01:02Z)
• I M Avatar: Implicit Morphable Head Avatars from Videos [68.13409777995392]
  We propose IMavatar, a novel method for learning implicit head avatars from monocular videos. Inspired by the fine-grained control mechanisms afforded by conventional 3DMMs, we represent the expression- and pose-related deformations via learned blendshapes and skinning fields. We show quantitatively and qualitatively that our method improves geometry and covers a more complete expression space compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-12-14T15:30:32Z)
• A Face Recognition System's Worst Morph Nightmare, Theoretically [0.0]
  Face Recognition Systems (FRSs) are vulnerable to morphing attacks, but most research focusses on landmark-based morphs. We propose a method to create a third, different type of morph, that has the advantage of being easier to train.
  arXiv  Detail & Related papers  (2021-11-30T14:09:47Z)
• Disentangled Lifespan Face Synthesis [100.29058545878341]
  A lifespan face synthesis (LFS) model aims to generate a set of photo-realistic face images of a person's whole life, given only one snapshot as reference. The generated face image given a target age code is expected to be age-sensitive reflected by bio-plausible transformations of shape and texture. This is achieved by extracting shape, texture and identity features separately from an encoder.
  arXiv  Detail & Related papers  (2021-08-05T22:33:14Z)
• Image Morphing with Perceptual Constraints and STN Alignment [70.38273150435928]
  We propose a conditional GAN morphing framework operating on a pair of input images. A special training protocol produces sequences of frames, combined with a perceptual similarity loss, promote smooth transformation over time. We provide comparisons to classic as well as latent space morphing techniques, and demonstrate that, given a set of images for self-supervision, our network learns to generate visually pleasing morphing effects.
  arXiv  Detail & Related papers  (2020-04-29T10:49:10Z)
• Fine-grained Image-to-Image Transformation towards Visual Recognition [102.51124181873101]
  We aim at transforming an image with a fine-grained category to synthesize new images that preserve the identity of the input image. We adopt a model based on generative adversarial networks to disentangle the identity related and unrelated factors of an image. Experiments on the CompCars and Multi-PIE datasets demonstrate that our model preserves the identity of the generated images much better than the state-of-the-art image-to-image transformation models.
  arXiv  Detail & Related papers  (2020-01-12T05:26:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.