Author name cluster

Xu Du

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

TIST Journal 2026 Journal Article

Adversarial Face Database against Deep Learning-Enabled Reconstruction Attacks

Hui Liu
Ling Ding
Jiageng Chen
Jinghua Wang
Xu Du
Jiabao Guo

Face recognition systems offer a range of applications that enhance security, efficiency, and personalization, e.g., access control, identity verification, and personalized services. Mainstream facial recognition systems employ the Edge-Cloud architecture to protect user privacy by storing facial feature data instead of original facial images. However, recently emerging reconstruction attacks based on deep learning can recover the visual information of original facial images from facial features, resulting in face privacy disclosure. Existing anti-reconstruction approaches either compromise facial recognition accuracy or fail to meet real-time requirements. In this article, we propose a practical privacy-preserving approach based on adversarial perturbations against reconstruction attacks. By incorporating subtle adversarial interference into facial features, the mapping relationship from facial features to original facial images is disrupted, and the baseline reconstruction networks cannot recover the original face image. We conducted experiments on two facial recognition models, FaceNet and ArcFace, both widely deployed in practical scenarios. The results show that the face recognition accuracy sacrifice of less than 1% can significantly reduce the quality of the reconstructed image. In terms of efficiency, the average time to generate an adversarial facial feature is less than 10 ms, meeting the real-time requirements of facial recognition.

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IROS Conference 2025 Conference Paper

Dual-Bubble Coordinated Acoustic Micromanipulator for Multidirectional Object Rotation *

Yuyang Li 0003
Zhongqiang Zhang
Chenglin Miao
Xu Du
Qiang Huang 0002
Tatsuo Arai
Xiaoming Liu 0007

Micromanipulation techniques struggle to achieve three-dimensional rotational control at the microscale without compromising biocompatibility or spatial flexibility. Conventional methods based on mechanical contact, optical forces, or confined microfluidics constrain dynamic reconfiguration and surgical accessibility. Here, we introduce a dual-bubble acoustic micromanipulator that enables multidirectional rotation through controlled hydrodynamic fields. By placing oscillating microbubbles at the tips of micropipettes, this system creates adjustable vortex patterns: a single microbubble generates toroidal flows for out-of-plane rotation, while two microbubbles produce shear forces for in-plane spinning. This approach uses simple mechanical adjustments to control rotational axes in open fluid environments, without needing frequency modulation or phase synchronization. Flow-field simulations and experiments with polystyrene microspheres confirm deterministic orientation control, and tests with shrimp embryos demonstrate rotation at clinically relevant speeds. The open architecture integrates seamlessly with standard microscopy and robotic injection systems, offering a non-contact, precise tool for applications such as polar body alignment, intracellular surgery, and 3-D imaging.

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EAAI Journal 2024 Journal Article

A hybrid deep learning method for the prediction of ship time headway using automatic identification system data

Quandang Ma
Xu Du
Cong Liu
Yuting Jiang
Zhao Liu
Zhe Xiao
Mingyang Zhang

Ship Time Headway (STH) is used in maritime navigation to describe the time interval between the arrivals of two consecutive ships in the same water area. This measurement may offer a straightforward way to gauge the frequency of ship traffic and the likelihood of congestion in a particular area. STH is an important factor in understanding and managing the dynamics of ship movements in busy waterways. This paper introduces a hybrid deep learning method for predicting STH in time domain. The method integrates the Seasonal-Trend Decomposition using Loess (STL), Multi-head Self-Attention (MSA) mechanism into Long Short-Term Memory (LSTM) neural network. The STH dataset was extracted from the Automatic Identification System (AIS) through ship trajectory spatial motion, and the seasonal, trend and residual components of the decomposition were then determined from the STH dataset using the STL algorithms. MSA-LSTM is adopted to comprehensively capture the evolving patterns of STH from the sequence. Comparison studies with existing methods demonstrate the accuracy and robustness of the predictions provided by this method, indicating that the proposed method outperforms other models in terms of prediction performance and learning capabilities. By predicting STH, the method offers potential to assist maritime traffic managers and navigators in assessing ship flow, thereby enabling them to make informed decisions on navigation safety and efficiency.

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