面向泛化性的形态攻击检测：基于一致性正则化的方法
Towards Generalizable Morph Attack Detection with Consistency Regularization
AI总结：
针对形态攻击检测泛化性不足的问题，本文提出基于两种形态专属增强的一致性正则化方法，从logit和嵌入层约束模型学习一致表征，显著提升了检测的泛化性与鲁棒性。
摘要：
尽管近期研究借助深度神经网络在形态攻击检测领域取得了显著进展，但这类方法往往难以很好地泛化到未见过的形态攻击上。随着各类形态攻击层出不穷，具备泛化能力的形态攻击检测已受到广泛关注。本文从一致性正则化的角度出发，聚焦于提升形态攻击检测的泛化能力。一致性正则化的核心前提是：具备泛化性的形态攻击检测模型，无论输入空间中出现何种可能的变化，都应当输出一致的预测结果。为实现这一目标，本研究提出了两种简单却有效的形态专属增强方法，用于在一致性正则化中探索真实形态变换的广阔空间。随后，研究对模型施加正则化约束，使其在经过大量形态增强的图像上，能够在logit层和嵌入层都学习到一致的表征。所提出的一致性正则化方法能够对齐模型隐藏层中，来自真实场景不同域生成的形态攻击图像的抽象特征。实验结果表明，与现有最优研究相比，本文提出的方法具备更优异的泛化性能和鲁棒性。
英文摘要：
Though recent studies have made significant progress in morph attack detection by virtue of deep neural networks, they often fail to generalize well to unseen morph attacks. With numerous morph attacks emerging frequently, generalizable morph attack detection has gained significant attention. This paper focuses on enhancing the generalization capability of morph attack detection from the perspective of consistency regularization. Consistency regularization operates under the premise that generalizable morph attack detection should output consistent predictions irrespective of the possible variations that may occur in the input space. In this work, to reach this objective, two simple yet effective morph-wise augmentations are proposed to explore a wide space of realistic morph transformations in our consistency regularization. Then, the model is regularized to learn consistently at the logit as well as embedding levels across a wide range of morph-wise augmented images. The proposed consistency regularization aligns the abstraction in the hidden layers of our model across the morph attack images which are generated from diverse domains in the wild. Experimental results demonstrate the superior generalization and robustness performance of our proposed method compared to the state-of-the-art studies.
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