DOOMGAN:High-Fidelity Dynamic Identity Obfuscation Ocular Generative Morphing
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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.
Key facts. Cited by 1 paper 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
22 of 64 references.
References · 22
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- 2015Autoencoding beyond pixels using a learned similarity metriccited by 407
- 2017VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learningcited by 200
- 2020Face Morphing Attack Generation & Detection: A Comprehensive Surveycited by 64
- 2021Eyes Tell All: Irregular Pupil Shapes Reveal GAN-generated Facescited by 48
- 2020Deep Face Representations for Differential Morphing Attack Detectioncited by 46
- 2020Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? -- Vulnerability and Detectioncited by 46
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- 2022Deep Learning for Iris Recognition: A Surveycited by 25
- 2017DenseNet for Dense Flowcited by 15
- 2022Morphing Attack Potentialcited by 15
- 2025Generalized Single-Image-Based Morphing Attack Detection Using Deep Representations from Vision Transformercited by 14
- 2020On the Influence of Ageing on Face Morph Attacks: Vulnerability and Detectioncited by 10
- 2022Periocular Biometrics: A Modality for Unconstrained Scenarioscited by 10
- 2020A New Periocular Dataset Collected by Mobile Devices in Unconstrained Scenarioscited by 7
- 2024MorCode: Face Morphing Attack Generation using Generative Codebookscited by 6
- 2020Real-time Pupil Tracking from Monocular Video for Digital Puppetrycited by 5
- 2024Synthesizing Iris Images using Generative Adversarial Networks: Survey and Comparative Analysiscited by 5
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Citing papers per year: 2026: 1.
1 paper.