DOOMGAN:High-Fidelity Dynamic Identity Obfuscation Ocular Generative Morphing

TL;DR

This paper introduces DOOMGAN, a novel generative model for high-fidelity visible-spectrum ocular morphing, enhancing biometric attack capabilities and providing a new dataset for research.

Contribution

We propose DOOMGAN, a landmark-driven, attention-guided generative model that improves ocular morphing realism and attack success rates, and release the first ocular morphing dataset.

Findings

Over 20% higher attack success rates compared to baselines

20% better elliptical iris structure generation

30% improved gaze consistency

Abstract

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.