3 citations · 7 across the 16 of their papers we have counts for
17 papers
SpikeTimer: Exploring Active Copyright Protection in Spiking Neural Networks via Temporal Backdoor Regularization
Xiao Yang, Gaolei Li, Jun Wu +2
Spiking Neural Networks (SNN) have emerged as a revolutionary paradigm compared to traditional Deep Neural Networks (DNN) in energy-efficient computing, showcasing exceptional capa…
Toward Polymorphic Backdoor against Semantic Communication via Intensity-Based Poisoning
Xiao Yang, Yuni Lai, Gaolei Li +4
Semantic Communication (SC) backdoor attacks aim to utilize triggers to manipulate the system into producing predetermined outputs via backdoored shared knowledge. Current SC backd…
Splats in Splats++: Robust and Generalizable 3D Gaussian Splatting Steganography
Yijia Guo, Wenkai Huang, Tong Hu +9
3D Gaussian Splatting (3DGS) has recently redefined the paradigm of 3D reconstruction, striking an unprecedented balance between visual fidelity and computational efficiency. As it…
Alkaid: Resilience to Edit Errors in Provably Secure Steganography via Distance-Constrained Encoding
Zhihan Cao, Gaolei Li, Jun Wu +3
While provably secure steganography provides strong concealment by ensuring stego carriers are indistinguishable from natural samples, such systems remain vulnerable to real-world…
SemCovert: Secure and Covert Video Transmission via Deep Semantic-Level Hiding
Zhihan Cao, Xiao Yang, Gaolei Li +3
Video semantic communication, praised for its transmission efficiency, still faces critical challenges related to privacy leakage. Traditional security techniques like steganograph…
Can Protective Watermarking Safeguard the Copyright of 3D Gaussian Splatting?
Wenkai Huang, Yijia Guo, Gaolei Li +6
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 si…
AuditVotes: Elevating Provable Defense for GNNs with Efficient Augmentation and Conditional Smoothing
Yuni Lai, Yulin Zhu, Yixuan Sun +6
Despite advancements in Graph Neural Networks (GNNs), adaptive attacks continue to challenge their robustness. Certified robustness via randomized smoothing offers provable guarant…
Splats in Splats: Robust and Effective 3D Steganography towards Gaussian Splatting
Yijia Guo, Wenkai Huang, Yang Li +6
3D Gaussian splatting (3DGS) has demonstrated impressive 3D reconstruction performance with explicit scene representations. Given the widespread application of 3DGS in 3D reconstru…
SFR-GNN: Simple and Fast Robust GNNs against Structural Attacks
Xing Ai, Guanyu Zhu, Yulin Zhu +4
Graph Neural Networks (GNNs) have demonstrated commendable performance for graph-structured data. Yet, GNNs are often vulnerable to adversarial structural attacks as embedding gene…
OpticGAI: Generative AI-aided Deep Reinforcement Learning for Optical Networks Optimization
Siyuan Li, Xi Lin, Yaju Liu +2
Deep Reinforcement Learning (DRL) is regarded as a promising tool for optical network optimization. However, the flexibility and efficiency of current DRL-based solutions for optic…
Graph Neural Backdoor: Fundamentals, Methodologies, Applications, and Future Directions
Xiao Yang, Gaolei Li, Jianhua Li
Graph Neural Networks (GNNs) have significantly advanced various downstream graph-relevant tasks, encompassing recommender systems, molecular structure prediction, social media ana…
Towards Multi-Task Generative-AI Edge Services with an Attention-based Diffusion DRL Approach
Yaju Liu, Xi Lin, Siyuan Li +3
As an emerging paradigm of content creation, AI-Generated Content (AIGC) has been widely adopted by a large number of edge end users. However, the requests for generated content fr…
Multi-Agent RL-Based Industrial AIGC Service Offloading over Wireless Edge Networks
Siyuan Li, Xi Lin, Hansong Xu +4
Currently, the generative model has garnered considerable attention due to its application in addressing the challenge of scarcity of abnormal samples in the industrial Internet of…
Trustworthy AI-Generative Content for Intelligent Network Service: Robustness, Security, and Fairness
Siyuan Li, Xi Lin, Yaju Liu +2
AI-generated content (AIGC) models, represented by large language models (LLM), have revolutionized content creation. High-speed next-generation communication technology is an idea…
Spikewhisper: Temporal Spike Backdoor Attacks on Federated Neuromorphic Learning over Low-power Devices
Hanqing Fu, Gaolei Li, Jun Wu +4
Federated neuromorphic learning (FedNL) leverages event-driven spiking neural networks and federated learning frameworks to effectively execute intelligent analysis tasks over amou…
Adversarial Robustness of Link Sign Prediction in Signed Graphs
Jialong Zhou, Xing Ai, Yuni Lai +7
Signed graphs serve as fundamental data structures for representing positive and negative relationships in social networks, with signed graph neural networks (SGNNs) emerging as th…
Towards Robust Graph Structural Learning Beyond Homophily via Preserving Neighbor Similarity
Yulin Zhu, Yuni Lai, Xing Ai +7
Despite the tremendous success of graph-based learning systems in handling structural data, it has been widely investigated that they are fragile to adversarial attacks on homophil…