# DOOMGAN:High-Fidelity Dynamic Identity Obfuscation Ocular Generative Morphing

arXiv: https://arxiv.org/abs/2507.17158 · HTML: https://mlsanity.com/paper/2507.17158/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 64 references that are in the map.
- First posted on arXiv 23 Jul 2025.

## Builds on (most cited first, 22 of 22)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [Autoencoding beyond pixels using a learned similarity metric](https://mlsanity.com/paper/1512.09300/) (2015)
- [VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning](https://mlsanity.com/paper/1705.07761/) (2017)
- [Face Morphing Attack Generation & Detection: A Comprehensive Survey](https://mlsanity.com/paper/2011.02045/) (2020)
- [Eyes Tell All: Irregular Pupil Shapes Reveal GAN-generated Faces](https://mlsanity.com/paper/2109.00162/) (2021)
- [Deep Face Representations for Differential Morphing Attack Detection](https://mlsanity.com/paper/2001.01202/) (2020)
- [Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? -- Vulnerability and Detection](https://mlsanity.com/paper/2007.03621/) (2020)
- [Deep Learning for Iris Recognition: A Survey](https://mlsanity.com/paper/2210.05866/) (2022)
- [DenseNet for Dense Flow](https://mlsanity.com/paper/1707.06316/) (2017)
- [Morphing Attack Potential](https://mlsanity.com/paper/2204.13374/) (2022)
- [Generalized Single-Image-Based Morphing Attack Detection Using Deep Representations from Vision Transformer](https://mlsanity.com/paper/2501.09817/) (2025)
- [On the Influence of Ageing on Face Morph Attacks: Vulnerability and Detection](https://mlsanity.com/paper/2007.02684/) (2020)
- [Periocular Biometrics: A Modality for Unconstrained Scenarios](https://mlsanity.com/paper/2212.13792/) (2022)
- [A New Periocular Dataset Collected by Mobile Devices in Unconstrained Scenarios](https://mlsanity.com/paper/2011.12427/) (2020)
- [MorCode: Face Morphing Attack Generation using Generative Codebooks](https://mlsanity.com/paper/2410.07625/) (2024)
- [Real-time Pupil Tracking from Monocular Video for Digital Puppetry](https://mlsanity.com/paper/2006.11341/) (2020)
- [Synthesizing Iris Images using Generative Adversarial Networks: Survey and Comparative Analysis](https://mlsanity.com/paper/2404.17105/) (2024)

## Built on by (most cited first, 1 of 1)

- [VoxMorph: Scalable Zero-shot Voice Identity Morphing via Disentangled Embeddings](https://mlsanity.com/paper/2601.20883/) (2026)

## Abstract (arXiv)

Ocular biometrics in the visible spectrum have emerged as a prominent modality due to their high accuracy, resistance to spoofing, and non-invasive nature. However, morphing attacks, synthetic biometric traits created by blending features from multiple individuals, threaten biometric system integrity. While extensively studied for near-infrared iris and face biometrics, morphing in visible-spectrum ocular data remains underexplored. Simulating such attacks demands advanced generation models that handle uncontrolled conditions while preserving detailed ocular features like iris boundaries and periocular textures. To address this gap, we introduce DOOMGAN, that encompasses landmark-driven encoding of visible ocular anatomy, attention-guided generation for realistic morph synthesis, and dynamic weighting of multi-faceted losses for optimized convergence. DOOMGAN achieves over 20% higher attack success rates than baseline methods under stringent thresholds, along with 20% better elliptical iris structure generation and 30% improved gaze consistency. We also release the first comprehensive ocular morphing dataset to support further research in this domain.