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Gaolei Li

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6 papers

1 author row

AAAI Conference 2026 Conference Paper

Can Protective Watermarking Safeguard the Copyright of 3D Gaussian Splatting?

Wenkai Huang
Yijia Guo
Gaolei Li
Lei Ma
Hang Zhang
Liwen Hu
Jiazheng Wang
Jianhua Li

3D Gaussian Splatting (3DGS) has emerged as a powerful representation for 3D scenes, widely adopted due to its exceptional efficiency and high-fidelity visual quality. Given the significant value of 3DGS assets, recent works have introduced specialized watermarking schemes to ensure copyright protection and ownership verification. However, can existing 3D Gaussian watermarking approaches genuinely guarantee robust protection of the 3D assets? In this paper, for the first time, we systematically explore and validate possible vulnerabilities of 3DGS watermarking frameworks. We demonstrate that conventional watermark removal techniques designed for 2D images do not effectively generalize to the 3DGS scenario due to the specialized rendering pipeline and unique attributes of each gaussian primitives. Motivated by this insight, we propose GSPure, the first watermark purification framework specifically for 3DGS watermarking representations. By analyzing view-dependent rendering contributions and exploiting geometrically accurate feature clustering, GSPure precisely isolates and effectively removes watermark-related Gaussian primitives while preserving scene integrity. Extensive experiments demonstrate that our GSPure achieves the best watermark purification performance, reducing watermark PSNR by up to 16.34dB while minimizing degradation to original scene fidelity with less than 1dB PSNR loss. Moreover, it consistently outperforms existing methods in both effectiveness and generalization.

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AAAI Conference 2026 Conference Paper

Splats in Splats: Robust and Effective 3D Steganography Towards Gaussian Splatting

Yijia Guo
Wenkai Huang
Yang Li
Gaolei Li
Hang Zhang
Liwen Hu
Jianhua Li
Tiejun Huang

3D Gaussian splatting (3DGS) has demonstrated impressive 3D reconstruction performance with explicit scene representations. Given the widespread application of 3DGS in 3D reconstruction and generation tasks, there is an urgent need to protect the copyright of 3DGS assets. However, existing copyright protection techniques for 3DGS overlook the usability of 3D assets, posing challenges for practical deployment. Here we describe splats in splats, the first 3DGS steganography framework that embeds 3D content in 3DGS itself without modifying any attributes. To achieve this, we take a deep insight into spherical harmonics (SH) and devise an importance-graded SH coefficient encryption strategy to embed the hidden SH coefficients. Furthermore, we employ a convolutional autoencoder to establish a mapping between the original Gaussian primitives' opacity and the hidden Gaussian primitives' opacity. Extensive experiments indicate that our method significantly outperforms existing 3D steganography techniques, with 5.31% higher scene fidelity and 3x faster rendering speed, while ensuring security, robustness, and user experience.

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IJCAI Conference 2025 Conference Paper

GraphProt: Certified Black-Box Shielding Against Backdoored Graph Models

Xiao Yang
Yuni Lai
Kai Zhou
Gaolei Li
Jianhua Li
Hang Zhang

Graph learning models have been empirically proven to be vulnerable to backdoor threats, wherein adversaries submit trigger-embedded inputs to manipulate the model predictions. Current graph backdoor defenses manifest several limitations: 1) dependence on model-related details, 2) necessitation of additional fine-tuning, and 3) reliance on extra explainability tools, all of which are infeasible under stringent privacy policies. To address those limitations, we propose GraphProt, a certified black-box defense method to suppress backdoor attacks on GNN-based graph classifiers. Our GraphProt operates in a model-agnostic manner and solely leverages graph input. Specifically, GraphProt first introduces designed topology-feature-filtration to mitigate graph anomalies. Subsequently, subgraphs are sampled via a formulated strategy integrating topology and features, followed by a robust model inference through a majority vote-based subgraph prediction ensemble. Our results across benchmark attacks and datasets show GraphProt effectively reduces attack success rates while preserving regular graph classification accuracy.

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IS Journal 2024 Journal Article

SupRTE: Suppressing Backdoor Injection in Federated Learning via Robust Trust Evaluation

Wenkai Huang
Gaolei Li
Xiaoyu Yi
Jianhua Li
Chengcheng Zhao
Ying Yin

This article proposes a novel scheme, SupRTE, to suppress backdoor injection in federated learning via robust trust evaluation, which effectively prevents malicious updates from infiltrating the model aggregation process. The robust trust evaluation process in SupRTE consists of two components: 1) the behavior representation extractor, which creates individual profiles for each client through multidimensional information, and 2) the trust scorer, which measures the discrepancies between malicious and benign clients as trust scores by utilizing grading and clustering strategies. According to these trust scores, SupRTE can dynamically adjust the weight of each participating client to effectively suppress the malicious backdoor injection. Remarkably, SupRTE can be easily deployed on the server without requiring any auxiliary information and is highly adaptable to various nonindependent identically distributed scenarios. Extensive experiments over three datasets against two kinds of backdoor variants are conducted. Experimental results demonstrate that SupRTE can significantly reduce the attack success rate to below 2% with a minimal impact on the main task accuracy and outperforms state-of-the-art defense methods.

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AAAI Conference 2024 Conference Paper

What Makes Good Collaborative Views? Contrastive Mutual Information Maximization for Multi-Agent Perception

Wanfang Su
Lixing Chen
Yang Bai
Xi Lin
Gaolei Li
Zhe Qu
Pan Zhou

Multi-agent perception (MAP) allows autonomous systems to understand complex environments by interpreting data from multiple sources. This paper investigates intermediate collaboration for MAP with a specific focus on exploring "good" properties of collaborative view (i.e., post-collaboration feature) and its underlying relationship to individual views (i.e., pre-collaboration features), which were treated as an opaque procedure by most existing works. We propose a novel framework named CMiMC (Contrastive Mutual Information Maximization for Collaborative Perception) for intermediate collaboration. The core philosophy of CMiMC is to preserve discriminative information of individual views in the collaborative view by maximizing mutual information between pre- and post-collaboration features while enhancing the efficacy of collaborative views by minimizing the loss function of downstream tasks. In particular, we define multi-view mutual information (MVMI) for intermediate collaboration that evaluates correlations between collaborative views and individual views on both global and local scales. We establish CMiMNet based on multi-view contrastive learning to realize estimation and maximization of MVMI, which assists the training of a collaborative encoder for voxel-level feature fusion. We evaluate CMiMC on V2X-Sim 1.0, and it improves the SOTA average precision by 3.08% and 4.44% at 0.5 and 0.7 IoU (Intersection-over-Union) thresholds, respectively. In addition, CMiMC can reduce communication volume to 1/32 while achieving performance comparable to SOTA. Code and Appendix are released at https://github.com/77SWF/CMiMC.

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AAAI Conference 2021 Conference Paper

DeHiB: Deep Hidden Backdoor Attack on Semi-supervised Learning via Adversarial Perturbation

Zhicong Yan
Gaolei Li
Yuan Tian
Jun Wu
Shenghong Li
Mingzhe Chen
H. Vincent Poor

The threat of data-poisoning backdoor attacks on learning algorithms typically comes from the labeled data used for learning. However, in deep semi-supervised learning (SSL), unknown threats mainly stem from unlabeled data. In this paper, we propose a novel deep hidden backdoor (DeHiB) attack for SSL-based systems. In contrast to the conventional attacking methods, the DeHiB can feed malicious unlabeled training data to the semi-supervised learner so as to enable the SSL model to output premeditated results. In particular, a robust adversarial perturbation generator regularized by a unified objective function is proposed to generate poisoned data. To alleviate the negative impact of trigger patterns on model accuracy and improve the attack success rate, a novel contrastive data poisoning strategy is designed. Using the proposed data poisoning scheme, one can implant the backdoor into the SSL model using the raw data without handcrafted labels. Extensive experiments based on CIFAR10 and CIFAR100 datasets demonstrates the effectiveness and crypticity of the proposed scheme.

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