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Zhi Lu

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6 papers

1 author row

TIST Journal 2026 Journal Article

Delete but Not Gone: Reactivation of Neural Network Watermarks

Hewang Nie
Jue Xiao
Zhi Lu
Renfei Shen
Songfeng Lu

As Deep Neural Networks (DNNs) become integral to critical applications, protecting their Intellectual Property (IP) has become paramount. Neural network watermarking is a technique that embeds unique identifiers into models, asserting ownership and deterring unauthorized use. However, sophisticated attacks can deactivate or remove these watermarks without significantly compromising model performance, undermining current protection strategies. In this article, we introduce the first method for reactivating deactivated neural network watermarks in altered DNN models without requiring access to the original model parameters or training data. By formulating the reactivation process as an optimization problem, we employ projected gradient descent to identify new trigger inputs that restore the embedded watermark. Regularization techniques are incorporated to ensure these triggers resemble legitimate inputs, enhancing both stealth and practicality. Through experiments on various benchmark datasets and model architectures, we demonstrate the effectiveness of our method against common model alterations, including fine-tuning, pruning, and surrogate model attacks. Our work addresses a critical gap in DNN IP protection, offering a robust and practical solution for watermark reactivation. This empowers model owners to assert their rights even in the face of advanced adversarial tactics.

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JBHI Journal 2026 Journal Article

Radar HRV Monitoring With Physiological Prior Inspired Deep Neural Networks

Haoyu Wang
Jinbo Chen
Dongheng Zhang
Zhi Lu
Yang Hu
Qibin Sun
Yan Chen

Radar sensing has emerged as a promising solution for the contactless monitoring of Heart Rate Variability (HRV), a crucial indicator of the cardiovascular and autonomic nervous systems. However, due to signal noise and interference that easily obscure heartbeat details, along with variations in heartbeat across different physiological conditions, existing methods remain restricted to laboratory settings with healthy subjects and fail in real-world scenarios involving more complex physiological conditions. In this study, we propose a physiological prior-inspired deep learning framework for robust radar-based HRV monitoring. Specifically, we leverage the prior that internal heartbeats drive movements across the entire torso surface and design a hybrid deep neural network to model the spatio-temporal relationship between full-body radio reflections and heartbeats, effectively mitigating interference. Then, we incorporate the cardiac motion's self-similarity prior to establish a signal augmentation strategy, effectively remodeling the HRV distribution and enhancing performance across diverse physiological conditions. We build and validate our method on a large-scale dataset comprising 7, 150 outpatients with complex physiological conditions in real-world scenarios. The experimental results demonstrate that our method achieves a mean IBI error of 19. 21 ms, an RMSSD error of 16. 23 ms, an SDSD error of 16. 70 ms, and a pNN50 error of 7. 28%. We further validate the performance by classifying five common cardiac conditions based on HRV results, demonstrating performance comparable to ECG-based methods. These results highlight the great potential of our approach for accurate, contactless HRV monitoring in real-world applications.

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JBHI Journal 2026 Journal Article

WN-Sleep: Modeling Whole-Night Data for Improved Sleep Staging Classification

Fang Zhou
Zhi Lu
Zhi Wu
Gaohan Ye
Lingjie Shu
Yu Pu
Beilei Wang
Dong Zhang

Sleep staging, crucial for diagnosing sleep disorders, requires precise recognition of physiological signals within 30-second epochs, a task fundamentally different from managing long-term semantic dependencies in natural language processing (NLP). Our model aims to refine the integration of local and global features for more accurate sleep stage classification. Following the American Academy of Sleep Medicine (AASM) guidelines, it focuses on rigorous intra-epoch feature extraction to ensure reliable identification of sleep stages. Moreover, our approach incorporates a global perspective by analyzing whole-night data, which is essential for handling transitional periods and ambiguities. Existing sequential modeling techniques often overlook the unique requirements of sleep staging, leading to performance declines when epochs extend beyond approximately 200. Our model addresses this by structurally processing local and global information and carefully balancing detailed intra-epoch analysis with an overarching view of sleep cycles through a gating mechanism. This gate mechanism selectively integrates long-term dependencies, optimizing the balance between local accuracy and global context. This approach represents a significant advancement over existing models, offering more accurate, reliable, and clinically relevant sleep staging. Extensive experiments on the SHHS, SleepEDF-20, and SleepEDF-78 datasets demonstrate that our method outperforms state-of-the-art approaches.

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IJCAI Conference 2024 Conference Paper

Learning-Based Tracking-before-Detect for RF-Based Unconstrained Indoor Human Tracking

Zhi Wu
Dongheng Zhang
Zixin Shang
Yuqin Yuan
Hanqin Gong
Binquan Wang
Zhi Lu
Yadong Li

Existing efforts on human tracking using wireless signal are primarily focused on constrained scenarios with only a few individuals in empty spaces. However, in practical unconstrained scenarios with severe interference and attenuation, accurate multi-person tracking has been intractable. In this paper, we propose NeuralTBD, utilizing the capability of deep models and advancement of Tracking-Before-Detect (TBD) methodology to achieve accurate human tracking. TBD is a classical tracking methodology from signal processing accumulating measurement in time domain to distinguish target traces from interference, which however relies on handcrafted shape/motion models, impeding efficacy in complex indoor scenarios. To tackle this challenge, we build an end-to-end learning-based TBD framework leverages the advanced modeling capabilities of deep models to significantly enhance the performance of TBD. To evaluate NeuralTBD, we collect an RF-based tracking dataset in unconstrained scenarios, which encompasses 4 million annotated radar frames with up to 19 individuals acting in 6 different scenarios. NeuralTBD realizes a 70% improvement in performance compared to conventional TBD methods. To our knowledge, this is the first attempt dealing with RF-based unconstrained human tracking. The code and dataset will be released.

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JBHI Journal 2017 Journal Article

Evaluation of Three Algorithms for the Segmentation of Overlapping Cervical Cells

Zhi Lu
Gustavo Carneiro
Andrew P. Bradley
Daniela Ushizima
Masoud S. Nosrati
Andrea G. C. Bianchi
Claudia M. Carneiro
Ghassan Hamarneh

In this paper, we introduce and evaluate the systems submitted to the first Overlapping Cervical Cytology Image Segmentation Challenge, held in conjunction with the IEEE International Symposium on Biomedical Imaging 2014. This challenge was organized to encourage the development and benchmarking of techniques capable of segmenting individual cells from overlapping cellular clumps in cervical cytology images, which is a prerequisite for the development of the next generation of computer-aided diagnosis systems for cervical cancer. In particular, these automated systems must detect and accurately segment both the nucleus and cytoplasm of each cell, even when they are clumped together and, hence, partially occluded. However, this is an unsolved problem due to the poor contrast of cytoplasm boundaries, the large variation in size and shape of cells, and the presence of debris and the large degree of cellular overlap. The challenge initially utilized a database of 16 high-resolution (×40 magnification) images of complex cellular fields of view, in which the isolated real cells were used to construct a database of 945 cervical cytology images synthesized with a varying number of cells and degree of overlap, in order to provide full access of the segmentation ground truth. These synthetic images were used to provide a reliable and comprehensive framework for quantitative evaluation on this segmentation problem. Results from the submitted methods demonstrate that all the methods are effective in the segmentation of clumps containing at most three cells, with overlap coefficients up to 0. 3. This highlights the intrinsic difficulty of this challenge and provides motivation for significant future improvement.

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IJCAI Conference 2017 Conference Paper

Sampling for Approximate Maximum Search in Factorized Tensor

Zhi Lu
Yang Hu
Bing Zeng

Factorization models have been extensively used for recovering the missing entries of a matrix or tensor. However, directly computing all of the entries using the learned factorization models is prohibitive when the size of the matrix/tensor is large. On the other hand, in many applications, such as collaborative filtering, we are only interested in a few entries that are the largest among them. In this work, we propose a sampling-based approach for finding the top entries of a tensor which is decomposed by the CANDECOMP/PARAFAC model. We develop an algorithm to sample the entries with probabilities proportional to their values. We further extend it to make the sampling proportional to the $k$-th power of the values, amplifying the focus on the top ones. We provide theoretical analysis of the sampling algorithm and evaluate its performance on several real-world data sets. Experimental results indicate that the proposed approach is orders of magnitude faster than exhaustive computing. When applied to the special case of searching in a matrix, it also requires fewer samples than the other state-of-the-art method.

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