提出DOOMGAN模型，实现高保真可见光眼球伪造攻击生成
DOOMGAN:High-Fidelity Dynamic Identity Obfuscation Ocular Generative Morphing
- 基于关键点编码与注意力机制生成真实眼球特征
- 攻击成功率提升20%，虹膜结构和注视一致性显著改善
- 适合生物识别安全研究者关注新型欺骗攻击
可见光眼生物特征因其高精度、抗伪造性和非侵入性成为重要模态。然而，通过融合多人特征生成的合成生物特征（即形态攻击）正威胁系统安全。尽管近红外虹膜和人脸形态攻击已有广泛研究，可见光眼球数据的相关研究仍不足。生成此类攻击需应对复杂环境，同时保留虹膜边界与周边纹理等细节。为此，本文提出DOOMGAN，结合关键点驱动的可见光眼球解码、注意力引导生成及多维度损失动态加权，实现更优收敛。实验表明，该方法在严格阈值下攻击成功率比基线高出20%，虹膜椭圆结构生成质量提升20%，注视方向一致性提高30%。同时，我们发布了首个综合性眼生物特征形态攻击数据集，以推动该领域研究。
原文摘要 · Abstract (English)
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.
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