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Shu Jiang

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6 papers
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6

AAAI Conference 2026 Conference Paper

Fading the Digital Ink: A Universal Black-Box Attack Framework for 3DGS Watermarking Systems

  • Qingyuan Zeng
  • Shu Jiang
  • Jiajing Lin
  • Zhenzhong Wang
  • Kay Chen Tan
  • Min Jiang

With the rise of 3D Gaussian Splatting (3DGS), a variety of digital watermarking techniques, embedding either 1D bitstreams or 2D images, are used for copyright protection. However, the robustness of these watermarking techniques against potential attacks remains underexplored. This paper introduces the first universal black-box attack framework, the Group-based Multi-objective Evolutionary Attack (GMEA), designed to challenge these watermarking systems. We formulate the attack as a large-scale multi-objective optimization problem, balancing watermark removal with visual quality. In a black-box setting, we introduce an indirect objective function that blinds the watermark detector by minimizing the standard deviation of features extracted by a convolutional network, thus rendering the feature maps uninformative. To manage the vast search space of 3DGS models, we employ a group-based optimization strategy to partition the model into multiple, independent sub-optimization problems. Experiments demonstrate that our framework effectively removes both 1D and 2D watermarks from mainstream 3DGS watermarking methods while maintaining high visual fidelity. This work reveals critical vulnerabilities in existing 3DGS copyright protection schemes and calls for the development of more robust watermarking systems.

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IJCAI Conference 2025 Conference Paper

SCNNs: Spike-based Coupling Neural Networks for Understanding Structural-Functional Relationships in the Human Brain

  • Shaolong Wei
  • Shu Jiang
  • Mingliang Wang
  • Liang Sun
  • Haonan Rao
  • Weiping Ding
  • Jiashuang Huang

Structural-functional coupling (SC-FC coupling) offers an effective approach for analyzing structural-functional relationships, capable of revealing the dependency of functional activity on the underlying white matter architecture. However, extant SC-FC coupling analysis methods primarily center on disclosing the statistical association between the topological patterns of structural connectivity (SC) and functional connectivity (FC), while often neglecting the neurobiological mechanisms by which the brain typically transmits and processes information in the form of spikes. To address this, we propose a biologically inspired deep-learning model called spike-based coupling neural networks (SCNNs). It can simulate spiking neural activity to more realistically reproduce the interaction between brain regions and the dynamic behavior of neuronal networks. Specifically, we first use spike neurons to capture the FC temporal characteristics of the original functional magnetic resonance imaging (fMRI) time series and the SC spatial characteristics of the structural brain network. Then, we use synaptic and neuronal filter effects to simulate the coupling mechanism of SC and FC in the brain at different temporal and spatial scales, thereby quantifying SC-FC coupling and providing support for the identification of brain diseases. The results on real datasets show that the proposed method can identify brain diseases and provide a new perspective for understanding SC-FC relationships.

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IROS Conference 2021 Conference Paper

A High-accuracy Framework for Vehicle Dynamic Modeling in Autonomous Driving

  • Shu Jiang
  • Yu Wang 0038
  • Weiman Lin
  • Yu Cao 0012
  • Longtao Lin
  • Jinghao Miao
  • Qi Luo

Vehicle dynamic models are the key to bridge the gap between simulation and real road test in autonomous driving. An accurate vehicle model allows control algorithms in simulation being transferred to real road test with same quality. In this paper, we present a dynamic model residual correction framework (DRF) for vehicle dynamic modeling. DRF provides a general accuracy improvement framework on existing vehicle dynamic models. On top of any existing open-loop dynamic model, this framework builds a Residual Correction Model (RCM) by integrating deep Neural Networks (NN) with Stochastic Variational Gaussian Process (SVGP) model. RCM takes a sequence of vehicle control commands and dynamic states for a certain time duration as modeling inputs, extracts underlying context from this sequence with deep encoder networks, and predicts open-loop dynamic model prediction errors. Five vehicle dynamic models are derived from DRF via encoder variations. Our contribution is consolidated with evaluation of the absolute trajectory error and the similarity between DRF outputs and the ground truth. Compared to classic rule-based and learning-based vehicle dynamic models, DRF accomplishes as high as 74. 12% to 85. 02% of the absolute trajectory error drop among all DRF variations.

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IROS Conference 2021 Conference Paper

Exploring Imitation Learning for Autonomous Driving with Feedback Synthesizer and Differentiable Rasterization

  • Jinyun Zhou
  • Rui Wang
  • Xu Liu
  • Yifei Jiang
  • Shu Jiang
  • Jiaming Tao
  • Jinghao Miao
  • Shiyu Song

We present a learning-based planner that aims to robustly drive a vehicle by mimicking human drivers’ driving behavior. We leverage a mid-to-mid approach that allows us to manipulate the input to our imitation learning network freely. With that in mind, we propose a novel feedback synthesizer for data augmentation. It allows our agent to gain more driving experience in various previously unseen environments that are likely to encounter, thus improving overall performance. This is in contrast to prior works that rely purely on random synthesizers. Furthermore, rather than completely commit to imitating, we introduce task losses that penalize undesirable behaviors, such as collision, off-road, and so on. Unlike prior works, this is done by introducing a differentiable vehicle rasterizer that directly converts the waypoints output by the network into images. This effectively avoids the usage of heavyweight ConvLSTM networks, therefore, yields a faster model inference time. About the network architecture, we exploit an attention mechanism that allows the network to reason critical objects in the scene and produce better interpretable attention heatmaps. To further enhance the safety and robustness of the network, we add an optional optimization-based post-processing planner improving the driving comfort. We comprehensively validate our method’s effectiveness in different scenarios that are specifically created for evaluating self-driving vehicles. Results demonstrate that our learning-based planner achieves high intelligence and can handle complex situations. Detailed ablation and visualization analysis are included to further demonstrate each of our proposed modules’ effectiveness in our method.

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