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Ling Ding

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8 papers

1 author row

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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AAAI Conference 2026 Conference Paper

ARNS: Adaptive Relation-Aware Negative Sampling with Curriculum Learning for Inductive Knowledge Graph Completion

Ling Ding
Zhizhi Yu
Di Jin
Lei Huang

Inductive knowledge graph completion (KGC) aims to predict missing links involving unseen entities, making it a particularly challenging task for knowledge representation learning. Traditional embedding-based methods often fall short in this setting due to their limited structural reasoning capabilities. Recently, Graph Neural Networks (GNNs) offer a promising alternative by explicitly modeling the graph topology. However, their performance heavily relies on the quality of negative samples during training, which significantly influences the learned representations and generalization ability. To tackle this issue, we propose Adaptive Relation-Aware Negative Sampling (ARNS), a negative sampling approach specifically tailored for GNN-based inductive KGC. It integrates three key strategies: (1) High-quality negatives via Linear WD for discriminative learning, (2) Relation-aware negatives utilizing relation graphs to preserve structural patterns, as well as (3) Adaptive curriculum learning that dynamically adjusts sampling ratios based on performance feedback. Our key innovation lies in a performance-driven adaptation mechanism that monitors training dynamics and modulates negative sample difficulty. This approach starts with easier samples for stability, and progressively introduces challenging negatives. Experiments demonstrate that ARNS outperforms state-of-the-art methods with significant MRR improvements while maintaining training stability. The adaptive design is particularly beneficial in inductive scenarios, where models can infer structural patterns from limited observations.

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AAAI Conference 2026 Conference Paper

Communication-efficient Multi-Agent Reinforcement Learning with Spatiotemporal Information Hub

Ling Ding
Tianbai Lyu
Zhiliang Bi
Hao Wang
Shanshan Feng
Wei Yu

Centralized training with decentralized execution (CTDE) is a framework for MARL with wide applications. In the CTDE paradigm, agents leverage global state information during training to mitigate the non-stationarity of the MARL environment, but must rely solely on partial observations during execution. Recent work has highlighted the growing importance of inter-agent communication for more effective learning and coordination. However, most existing methods overlook the fact that real-world communication channels are often bandwidth-constrained and imperfectly reliable. Toward more communication-efficient and robust MARL, we extend the conventional CTDE framework with an information hub. The hub collects local observations from the agents to restore the global state, which is then delivered to the agents on demand. To this end, technical mechanisms are designed to enable effective global reconstruction with incomplete observations, as well as agent-specific attention to the reconstructed global information. Experiments on multiple cooperative MARL benchmarks demonstrate that our method achieves state-of-the-art performance compared to popular MARL algorithms while substantially reducing communication overhead and exhibiting strong robustness under imperfect communication channels.

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EAAI Journal 2025 Journal Article

Enhanced air pollution spatiotemporal forecast model using frequency domain convolution and attention mechanism

Haiwei Yang
Ru Yang
Ling Ding
Shiqiang Du
Maozhen Li
Bo Zhang

Accurate forecasting of air pollution, which is crucial for public health and environmental management, often faces challenges in effectively capturing the complex and intertwined spatiotemporal dynamics of pollutants. Existing models frequently struggle to simultaneously account for broad periodic spatiotemporal dependencies as well as fine-grained local temporal patterns. This paper presents a novel deep learning architecture, the Fourier Convolutional Graph Transformer (FCGformer), specifically designed to overcome these limitations. FCGformer distinctively features a dual-module approach: a Global Module that constructs an integrated spatiotemporal graph and leverages Fourier transforms with frequency domain convolution to extract long-range dependencies and crucial periodicities; and a Local Module that employs inverse temporal embedding and self-attention to meticulously capture nuanced, short-term temporal variations. The key contribution of this work lies in the synergistic integration that enables FCGformer to effectively model complex pollutant behaviors, providing a more comprehensive understanding of both global contexts and local details. Extensive experiments demonstrate that FCGformer significantly outperforms state-of-the-art benchmark models in prediction accuracy, offering a promising advancement for improved air quality management.

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AAAI Conference 2025 Conference Paper

Towards Global-Topology Relation Graph for Inductive Knowledge Graph Completion

Ling Ding
Lei Huang
Zhizhi Yu
Di Jin
Dongxiao He

Knowledge Graphs (KGs) are structured data presented as directed graphs. Due to the common issues of incompleteness and inaccuracy encountered during construction and maintenance, completing KGs becomes a critical task. Inductive Knowledge Graph Completion (KGC) excels at inferring patterns or models from seen data to be applied to unseen data. However, existing methods mainly focus on new entities, while relations are usually randomly initialized. To this end, we propose TARGI, a simple yet effective inductive method for KGC. Specifically, we first construct a global relation graph for each topology from a global graph perspective, thus leveraging the in-variance of relation structures. We then utilize this graph to aggregate the rich embeddings of new relations and new entities, thereby performing KGC robustly in inductive scenarios. This successfully addresses the excessive reliance on the degree of relations and resolves the high complexity and limited scope of enclosing subgraph sampling in existing fully inductive algorithms. We conduct KGC experiments on six inductive datasets using inference data where entities are entirely new and new relations at 100 percent, 50 percent, and 0 percent radios. Extensive results demonstrate that our model accurately learns the topological structures and embeddings of new relations, and guides the embedding learning of new entities. Notably, our model outperforms 15 SOTA methods, especially in two fully inductive datasets.

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AAAI Conference 2024 Conference Paper

Expressive Multi-Agent Communication via Identity-Aware Learning

Wei Du
Shifei Ding
Lili Guo
Jian Zhang
Ling Ding

Information sharing through communication is essential for tackling complex multi-agent reinforcement learning tasks. Many existing multi-agent communication protocols can be viewed as instances of message passing graph neural networks (GNNs). However, due to the significantly limited expressive ability of the standard GNN method, the agent feature representations remain similar and indistinguishable even though the agents have different neighborhood structures. This further results in the homogenization of agent behaviors and reduces the capability to solve tasks effectively. In this paper, we propose a multi-agent communication protocol via identity-aware learning (IDEAL), which explicitly enhances the distinguishability of agent feature representations to break the diversity bottleneck. Specifically, IDEAL extends existing multi-agent communication protocols by inductively considering the agents' identities during the message passing process. To obtain expressive feature representations for a given agent, IDEAL first extracts the ego network centered around that agent and then performs multiple rounds of heterogeneous message passing, where different parameter sets are applied to the central agent and the other surrounding agents within the ego network. IDEAL fosters expressive communication between agents and generates distinguishable feature representations, which promotes action diversity and individuality emergence. Experimental results on various benchmarks demonstrate IDEAL can be flexibly integrated into various multi-agent communication methods and enhances the corresponding performance.

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AAAI Conference 2024 Conference Paper

Learning Efficient and Robust Multi-Agent Communication via Graph Information Bottleneck

Shifei Ding
Wei Du
Ling Ding
Lili Guo
Jian Zhang

Efficient communication learning among agents has been shown crucial for cooperative multi-agent reinforcement learning (MARL), as it can promote the action coordination of agents and ultimately improve performance. Graph neural network (GNN) provide a general paradigm for communication learning, which consider agents and communication channels as nodes and edges in a graph, with the action selection corresponding to node labeling. Under such paradigm, an agent aggregates information from neighbor agents, which can reduce uncertainty in local decision-making and induce implicit action coordination. However, this communication paradigm is vulnerable to adversarial attacks and noise, and how to learn robust and efficient communication under perturbations has largely not been studied. To this end, this paper introduces a novel Multi-Agent communication mechanism via Graph Information bottleneck (MAGI), which can optimally balance the robustness and expressiveness of the message representation learned by agents. This communication mechanism is aim at learning the minimal sufficient message representation for an agent by maximizing the mutual information (MI) between the message representation and the selected action, and simultaneously constraining the MI between the message representation and the agent feature. Empirical results demonstrate that MAGI is more robust and efficient than state-of-the-art GNN-based MARL methods.

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EAAI Journal 2023 Journal Article

Active diversification of head-class features in bilateral-expert models for enhanced tail-class optimization in long-tailed classification

Jianting Chen
Ling Ding
Yunxiao Yang
Yang Xiang

Training deep learning models on long-tailed datasets is a challenging task since the classification performance of tail classes with fewer samples is always unsatisfactory. Currently, many long-tailed methods have achieved success. However, some methods always improve tail-class performance at the expense of head-class performance due to limited model capability. To address this issue, we propose a novel algorithm-level method inspired by information theory to balance the information space of each class and boost tail-class performance while minimizing head-class sacrifice. Our method involves actively eliminating the redundant feature information of head classes to save space for tail classes during training. Specifically, we use a bilateral-expert model and design a duplicate information disentanglement (DID) module that can extract duplicate and redundant information from bilateral-expert features. This allows us to develop a head diversity loss to decrease the extracted duplicate and redundant information of head classes and a tail distillation loss to increase the label information of tail classes. The joint result of these two losses allows our model to fully leverage the information space for improved tail-class performance without compromising head-class performance. The effectiveness and practicability of our method are verified by five datasets with long-tailed distributions for visual recognition or fault diagnosis tasks. Experimental results demonstrate that our method outperforms currently available mainstream methods, which we attribute to the effectiveness of our proposed DID module and the incorporation of two long-tailed losses.

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