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Jinghua Wang

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7 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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NeurIPS Conference 2025 Conference Paper

Hierarchical Information Aggregation for Incomplete Multimodal Alzheimer's Disease Diagnosis

Chengliang Liu
Que Yuanxi
Qihao Xu
Yabo Liu
Jie Wen
Jinghua Wang
Xiaoling Luo

Alzheimer's Disease (AD) poses a significant health threat to the aging population, underscoring the critical need for early diagnosis to delay disease progression and improve patient quality of life. Recent advances in heterogeneous multimodal artificial intelligence (AI) have facilitated comprehensive joint diagnosis, yet practical clinical scenarios frequently encounter incomplete modalities due to factors like high acquisition costs or radiation risks. Moreover, traditional convolution-based architecture face inherent limitations in capturing long-range dependencies and handling heterogeneous medical data efficiently. To address these challenges, in our proposed heterogeneous multimodal diagnostic framework (HAD), we develop a multi-view Hilbert curve-based Mamba block and a hierarchical spatial feature extraction module to simultaneously capture local spatial features and global dependencies, effectively alleviating spatial discontinuities introduced by voxel serialization. Furthermore, to balance semantic consistency and modal specificity, we build a unified mutual information learning objective in the heterogeneous multimodal embedding space, which maintains effective learning of modality-specific information to avoid modality collapse caused by model preference. Extensive experiments demonstrate that our HAD significantly outperforms state-of-the-art methods in various modality-missing scenarios, providing an efficient and reliable solution for early-stage AD diagnosis.

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YNIMG Journal 2025 Journal Article

Mapping subtype-specific disease epicenters and brain aging characteristics in major depressive disorder through normative model-driven analysis of brain structural alterations

Peng Wang
Yuhong Zheng
Li Sun
Yang Xiao
Xuelian Zang
Jinghua Wang
Jinhui Wang
Shao-Wei Xue

Major depressive disorder (MDD), a prevalent mental health condition, manifests intricate alterations in brain structure that evolve gradually over time and across various brain regions. Despite significant research efforts, two fundamental questions remain unsettled: the precise brain origins of MDD and whether MDD contributes to accelerates brain aging. To this end, we conducted a comprehensive investigation leveraging data from 830 MDD patients and 853 matched healthy controls (HC). Normative models, established on HC gray matter volume (GMV) data, were utilized to quantify individual deviations in GMV among MDD patients. Employing k-means clustering to these deviation profiles, we successfully discerned two clinically distinct subtypes. Subtype 1 is characterized by GMV atrophy, coupled with indications of accelerated brain aging processes. In contrast, subtype 2 exhibits increased GMV without significant acceleration of aging phenomena. Intriguingly, both subtypes converge on the default mode network as a common disease epicenter, highlighting a shared neurophysiological underpinning. However, subtype-specific epicenters diverge, with subtype 1 featuring unique foci primarily in the hippocampus and amygdala, whereas subtype 2 distinguishes itself with epicenters primarily located in the accumbens. This nuanced examination of subtype-specific brain alterations, incorporating their intricate spatiotemporal dynamics, provides profound insights into the heterogeneity and complexity inherent in MDD.

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ICML Conference 2025 Conference Paper

Mutual Learning for SAM Adaptation: A Dual Collaborative Network Framework for Source-Free Domain Transfer

Yabo Liu
Waikeung Wong
Chengliang Liu 0003
Xiaoling Luo 0001
Yong Xu 0001
Jinghua Wang

Segment Anything Model (SAM) has demonstrated remarkable zero-shot segmentation capabilities across various visual tasks. However, its performance degrades significantly when deployed in new target domains with substantial distribution shifts. While existing self-training methods based on fixed teacher-student architectures have shown improvements, they struggle to ensure that the teacher network consistently outperforms the student under severe domain shifts. To address this limitation, we propose a novel Collaborative Mutual Learning Framework for source-free SAM adaptation, leveraging dual-networks in a dynamic and cooperative manner. Unlike fixed teacher-student paradigms, our method dynamically assigns the teacher and student roles by evaluating the reliability of each collaborative network in each training iteration. Our framework incorporates a dynamic mutual learning mechanism with three key components: a direct alignment loss for knowledge transfer, a reverse distillation loss to encourage diversity, and a triplet relationship loss to refine feature representations. These components enhance the adaptation capabilities of the collaborative networks, enabling them to generalize effectively to target domains while preserving their pre-trained knowledge. Extensive experiments on diverse target domains demonstrate that our proposed framework achieves state-of-the-art adaptation performance.

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NeurIPS Conference 2025 Conference Paper

QiMeng-CodeV-R1: Reasoning-Enhanced Verilog Generation

Yaoyu Zhu
Di Huang
Hanqi Lyu
Xiaoyun Zhang
Chongxiao Li
Wenxuan Shi
Yutong Wu
Jianan Mu

Large language models (LLMs) trained via reinforcement learning with verifiable reward (RLVR) have achieved breakthroughs on tasks with explicit, automatable verification, such as software programming and mathematical problems. Extending RLVR to electronic design automation (EDA), especially automatically generating hardware description languages (HDLs) like Verilog from natural-language (NL) specifications, however, poses three key challenges: the lack of automated and accurate verification environments, the scarcity of high-quality NL-code pairs, and the prohibitive computation cost of RLVR. To this end, we introduce CodeV-R1, an RLVR framework for training Verilog generation LLMs. First, we develop a rule-based testbench generator that performs robust equivalence checking against golden references. Second, we propose a round-trip data synthesis method that pairs open-source Verilog snippets with LLM-generated NL descriptions, verifies code–NL–code consistency via the generated testbench, and filters out inequivalent examples to yield a high-quality dataset. Third, we employ a two-stage "distill-then-RL" training pipeline: distillation for the cold start of reasoning abilities, followed by adaptive DAPO, our novel RLVR algorithm that can reduce training cost by adaptively adjusting sampling rate. The resulting model, CodeV-R1-7B, achieves 68. 6 \% and 72. 9 \% pass@1 on VerilogEval v2 and RTLLM v1. 1, respectively, surpassing prior state-of-the-art by 12$\sim$20 \%, while even exceeding the performance of 671B DeepSeek-R1 on RTLLM. We have released our model, training code, and dataset to facilitate research in EDA and LLM communities.

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NeurIPS Conference 2025 Conference Paper

RFMPose: Generative Category-level Object Pose Estimation via Riemannian Flow Matching

Wenzhe Ouyang
Qi Ye
Jinghua Wang
Zenglin Xu
Jiming Chen

We introduce RFMPose, a novel generative framework for category-level 6D object pose estimation that learns deterministic pose trajectories through Riemannian Flow Matching (RFM). Existing discriminative approaches struggle with multi-hypothesis predictions (e. g. , symmetry ambiguities) and often require specialized network architectures. RFMPose advances this paradigm through three key innovations: (1) Ensuring geometric consistency via geodesic interpolation on Riemannian manifolds combined with bi-invariant metric constraints; (2) Alleviating symmetry-induced ambiguities through Riemannian Optimal Transport for probability mass redistribution without ad-hoc design; (3) Enabling end-to-end likelihood estimation through Hutchinson trace approximation, thereby eliminating auxiliary model dependencies. Extensive experiments on the Omni6DPose demonstrate state-of-the-art performance of the proposed method, with significant improvements of $\textbf{+4. 1}$ in $\mathrm{\textbf{IoU}_{25}}$ and $\textbf{+2. 4}$ in $\textbf{5°2cm}$ metrics compared to prior generative approaches. Furthermore, the proposed RFM framework exhibits robust sim-to-real transfer capabilities and facilitates pose tracking extensions with minimal architectural adaptation.

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JBHI Journal 2024 Journal Article

A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation

Jiesi Hu
Yanwu Yang
Xutao Guo
Ting Ma
Jinghua Wang

Source-free domain adaptation (SFDA) aims to adapt models trained on a labeled source domain to an unlabeled target domain without access to source data. In medical imaging scenarios, the practical significance of SFDA methods has been emphasized due to data heterogeneity and privacy concerns. Recent state-of-the-art SFDA methods primarily rely on self-training based on pseudo-labels (PLs). Unfortunately, the accuracy of PLs may deteriorate due to domain shift, thus limiting the effectiveness of the adaptation process. To address this issue, we propose a Chebyshev confidence guided SFDA framework to accurately assess the reliability of PLs and generate self-improving PLs for self-training. The Chebyshev confidence is estimated by calculating the probability lower bound of PL confidence, given the prediction and the corresponding uncertainty. Leveraging the Chebyshev confidence, we introduce two confidence-guided denoising methods: direct denoising and prototypical denoising. Additionally, we propose a novel teacher-student joint training scheme (TJTS) that incorporates a confidence weighting module to iteratively improve PLs' accuracy. The TJTS, in collaboration with the denoising methods, effectively prevents the propagation of noise and enhances the accuracy of PLs. Extensive experiments in diverse domain scenarios validate the effectiveness of our proposed framework and establish its superiority over state-of-the-art SFDA methods. Our paper contributes to the field of SFDA by providing a novel approach for precisely estimating the reliability of PLs and a framework for obtaining high-quality PLs, resulting in improved adaptation performance.

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