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J. T. Kwok

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Aug 2026

Deep Bio-Hashing Network for Privacy-Preserving Cancelable Finger Vein Recognition.

Finger vein recognition technology has become one of the primary solutions for high-security identification systems. However, traditional finger vein recognition methods face several limitations, such as the risk of permanent identity loss due to biometric data leakage. Through designing cancelable biometrics, users' privacy and security can be further protected, and the risk of biometric data misuse can be reduced. In this paper, we propose Deep Bio-Hashing Network (DBHN) for privacy-preserving finger vein recognition, achieving end-to-end cancelable recognition. Specifically, we design a class center alignment module to improve feature alignment, which aligns the variations of all potential finger views with the finger center view via a learnable transformation. Furthermore, to tackle the security problem caused by stolen tokenized random numbers, a Deep Bio-Hashing layer is proposed, which utilizes a system-level token instead of assigning unique tokens to each user. To supervise the learning process of DBHN, we design a hybrid loss function including classification loss, consistency-based localization loss, and class center triplet loss. Finally, we conduct experiments and analysis on three publicly available datasets. Experimental results show that our method has favorable recognition performance and achieves competitive results compared to state-of-the-art hash-based methods. The analysis verifies the cancelable biometrics attributes and justifies the resilience of the method against existing security and privacy attacks.

Jie Gui, Yifan Wang, Minjing Dong et al. · 0 citations
2026

PANDA: Diffusion-Guided Purification and Adaptation for Robust Point Cloud Classification Against Adversarial Attack

Deep learning models for point cloud classification are highly vulnerable to adversarial attacks, while recent advances in diffusion-based purification have shown promising defensive performance. However, existing diffusion-based purification methods harbor two fundamental limitations. First, a distributional gap arises from their training on clean-to-clean paths, which fails to generalize to the required adversarial-to-clean transition. Second, a semantic mismatch occurs because the fixed victim classifier cannot adapt to the decision boundaries of the purified data distribution. To address this, we propose PANDA, a two-stage framework that combines robust purification with classifier adaptation. For purification, we introduce PANDA-P, a novel dual-branch diffusion training strategy that simultaneously optimizes on both clean-to-clean and adversarial-to-clean paths. This unified formulation boosts the purification effectiveness while preserving fidelity. For adaptation, we design PANDA-A, a fine-tuning scheme that leverages a consistency-driven learning objective to reshape the classifier’s feature space and recalibrate a robust decision boundary for the purified data. Extensive experiments show that PANDA achieves consistently superior robustness over existing purification-based defenses on both synthetic and real-world benchmarks.

Yu-Xin Zhang, Xiaofeng Cong, Minjing Dong et al. · 0 citations