标题： DOOMGAN：高保真动态身份混淆眼动生成变形 显示英文标题

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

摘要： 可见光谱中的眼部生物特征已成为一种显著的模态，因其高准确性、抗欺骗性和非侵入性。 然而，变形攻击，即通过融合多个个体的特征生成的合成生物特征，威胁着生物特征系统的完整性。 尽管在近红外虹膜和面部生物特征方面进行了广泛研究，但可见光谱眼部数据中的变形仍研究不足。 模拟此类攻击需要先进的生成模型，在处理不受控制条件的同时保留详细的眼部特征，如虹膜边界和眼周纹理。 为解决这一问题，我们引入了DOOMGAN，它包括可见眼部解剖结构的基于关键点的编码、用于现实变形合成的注意力引导生成，以及多方面损失的动态加权以实现优化收敛。 在严格阈值下，DOOMGAN的攻击成功率比基线方法高出20%以上，同时椭圆形虹膜结构生成提高了20%，凝视一致性改善了30%。 我们还发布了首个全面的眼部变形数据集，以支持该领域的进一步研究。 显示英文摘要

摘要： 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.