Selected work

Representative Papers

Identity Card Presentation Attack Detection: A Systematic Review

Nov 08, 2025

This study systematically reviews AI-driven Presentation Attack Detection (AI-PAD) research from 2020–2025, identifying two critical challenges: (1) data scarcity severely limits model generalizability across diverse identity documents and emerging attack modalities; and (2) a “reality gap” (performance discrepancies between private and public benchmark evaluations) and a “synthetic utility gap” (synthetic data failing to capture real-world forensic utility, leading to artifact overfitting). Adopting the PRISMA framework, we classify and evaluate methods spanning deep learning, fine-grained forgery trace analysis, and foundation models. We formally define and empirically validate these dual gaps for the first time. Our work establishes a reproducible, generalizable PAD research paradigm, clarifies the technical evolution trajectory, identifies key research gaps, and proposes a forward-looking roadmap toward secure, robust, and globally applicable AI-PAD systems.

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ProfileXAI: User-Adaptive Explainable AI

Oct 27, 2025

To address heterogeneous user requirements for model interpretability, this paper proposes a model- and domain-agnostic explainable AI (XAI) framework. Methodologically, it integrates post-hoc explanation techniques (SHAP, LIME, Anchor) with retrieval-augmented large language models (LLMs), implementing a user-profile-conditioned explanation mechanism that dynamically selects the optimal explainer. Explanations are generated via multimodal knowledge base indexing and conversational prompt engineering, yielding natural-language outputs with low redundancy and high fidelity. The key contributions are personalized explanation strategy adaptation and cross-user consistency preservation. Experimental evaluation on heart disease and thyroid cancer datasets demonstrates complementary strengths among explainers: average user satisfaction reaches 4.1/5, expert-assessed explanation quality scores 3.77/5, and token consumption remains stable (σ ≤ 13%).

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Recent publications

Latest Papers

Identity Card Presentation Attack Detection: A Systematic Review

Nov 08, 2025

This study systematically reviews AI-driven Presentation Attack Detection (AI-PAD) research from 2020–2025, identifying two critical challenges: (1) data scarcity severely limits model generalizability across diverse identity documents and emerging attack modalities; and (2) a “reality gap” (performance discrepancies between private and public benchmark evaluations) and a “synthetic utility gap” (synthetic data failing to capture real-world forensic utility, leading to artifact overfitting). Adopting the PRISMA framework, we classify and evaluate methods spanning deep learning, fine-grained forgery trace analysis, and foundation models. We formally define and empirically validate these dual gaps for the first time. Our work establishes a reproducible, generalizable PAD research paradigm, clarifies the technical evolution trajectory, identifies key research gaps, and proposes a forward-looking roadmap toward secure, robust, and globally applicable AI-PAD systems.

0 citationsRead paper

ProfileXAI: User-Adaptive Explainable AI

Oct 27, 2025

To address heterogeneous user requirements for model interpretability, this paper proposes a model- and domain-agnostic explainable AI (XAI) framework. Methodologically, it integrates post-hoc explanation techniques (SHAP, LIME, Anchor) with retrieval-augmented large language models (LLMs), implementing a user-profile-conditioned explanation mechanism that dynamically selects the optimal explainer. Explanations are generated via multimodal knowledge base indexing and conversational prompt engineering, yielding natural-language outputs with low redundancy and high fidelity. The key contributions are personalized explanation strategy adaptation and cross-user consistency preservation. Experimental evaluation on heart disease and thyroid cancer datasets demonstrates complementary strengths among explainers: average user satisfaction reaches 4.1/5, expert-assessed explanation quality scores 3.77/5, and token consumption remains stable (σ ≤ 13%).

0 citationsRead paper