Institution profile

University of Technology

Academic institution
Research library8linked papers
Opportunities0open roles
Selected work

Representative Papers

Deep learning Based Correction Algorithms for 3D Medical Reconstruction in Computed Tomography and Macroscopic Imaging

Jan 30, 2026

This work addresses the challenges of low reconstruction accuracy and poor generalization in 3D organ modeling from macroscopic slice imaging, which arise due to data scarcity and large deformations. To overcome these limitations, the authors propose a two-stage hybrid registration framework: an initial global rigid alignment is achieved through Optimal Slice Matching (OCM) combined with Hough transform, followed by local non-rigid deformation estimation using explicit geometric priors integrated into a lightweight, modified VoxelMorph network. By hierarchically decoupling global optimization from local refinement, the method significantly outperforms single-stage baselines—even when trained on only 40 kidney specimens—yielding more accurate, anatomically plausible, and reproducible multimodal 3D reconstructions suitable for surgical planning and medical education.

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A High-Performance Fractal Encryption Framework and Modern Innovations for Secure Image Transmission

Jan 28, 2026

This work proposes a novel image encryption framework that integrates fractal geometry with Fourier transform, addressing the longstanding challenge of simultaneously achieving high security, image fidelity, and computational efficiency in traditional methods. By introducing fractal structures into the frequency-domain encryption process for the first time, the proposed approach effectively overcomes the trade-off bottleneck between efficiency and reconstruction quality inherent in conventional schemes. Experimental results demonstrate that the method significantly accelerates encryption and decryption while preserving excellent image reconstruction fidelity, thereby offering both strong security and practical utility. These findings underscore its potential advantage for efficient and secure image transmission in real-world applications.

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A Hybrid CAPTCHA Combining Generative AI with Keystroke Dynamics for Enhanced Bot Detection

Sep 29, 2025

Traditional CAPTCHAs struggle to balance security and usability. This paper proposes a novel hybrid CAPTCHA system integrating generative AI with keystroke dynamics: large language models (LLMs) dynamically generate semantic cognition challenges, while users’ keystroke timing features are simultaneously captured and analyzed to establish a dual-modal “cognitive-behavioral” discrimination mechanism. To our knowledge, this is the first work unifying LLM-driven dynamic semantic verification with biometric-level input rhythm analysis, effectively thwarting paste attacks, scripted automation, and end-to-end AI-based bypasses. Experimental results show that the system achieves a 92.3% human success rate—indicating excellent usability—while maintaining a 99.1% bot detection accuracy, significantly outperforming state-of-the-art text- and image-based CAPTCHAs. This work establishes a scalable, adaptively robust multimodal security paradigm for next-generation human-bot differentiation.

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Recent publications

Latest Papers

Deep learning Based Correction Algorithms for 3D Medical Reconstruction in Computed Tomography and Macroscopic Imaging

Jan 30, 2026

This work addresses the challenges of low reconstruction accuracy and poor generalization in 3D organ modeling from macroscopic slice imaging, which arise due to data scarcity and large deformations. To overcome these limitations, the authors propose a two-stage hybrid registration framework: an initial global rigid alignment is achieved through Optimal Slice Matching (OCM) combined with Hough transform, followed by local non-rigid deformation estimation using explicit geometric priors integrated into a lightweight, modified VoxelMorph network. By hierarchically decoupling global optimization from local refinement, the method significantly outperforms single-stage baselines—even when trained on only 40 kidney specimens—yielding more accurate, anatomically plausible, and reproducible multimodal 3D reconstructions suitable for surgical planning and medical education.

0 citationsRead paper

A High-Performance Fractal Encryption Framework and Modern Innovations for Secure Image Transmission

Jan 28, 2026

This work proposes a novel image encryption framework that integrates fractal geometry with Fourier transform, addressing the longstanding challenge of simultaneously achieving high security, image fidelity, and computational efficiency in traditional methods. By introducing fractal structures into the frequency-domain encryption process for the first time, the proposed approach effectively overcomes the trade-off bottleneck between efficiency and reconstruction quality inherent in conventional schemes. Experimental results demonstrate that the method significantly accelerates encryption and decryption while preserving excellent image reconstruction fidelity, thereby offering both strong security and practical utility. These findings underscore its potential advantage for efficient and secure image transmission in real-world applications.

0 citationsRead paper

A Hybrid CAPTCHA Combining Generative AI with Keystroke Dynamics for Enhanced Bot Detection

Sep 29, 2025

Traditional CAPTCHAs struggle to balance security and usability. This paper proposes a novel hybrid CAPTCHA system integrating generative AI with keystroke dynamics: large language models (LLMs) dynamically generate semantic cognition challenges, while users’ keystroke timing features are simultaneously captured and analyzed to establish a dual-modal “cognitive-behavioral” discrimination mechanism. To our knowledge, this is the first work unifying LLM-driven dynamic semantic verification with biometric-level input rhythm analysis, effectively thwarting paste attacks, scripted automation, and end-to-end AI-based bypasses. Experimental results show that the system achieves a 92.3% human success rate—indicating excellent usability—while maintaining a 99.1% bot detection accuracy, significantly outperforming state-of-the-art text- and image-based CAPTCHAs. This work establishes a scalable, adaptively robust multimodal security paradigm for next-generation human-bot differentiation.

0 citationsRead paper