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Found 24 papers, 4 papers with code
AuditVotes: A Framework Towards More Deployable Certified Robustness for Graph Neural Networks
This excessive randomization degrades data quality and disrupts prediction consistency, limiting the practical deployment of certifiably robust GNNs in real-world scenarios where both accuracy and robustness are essential.
Splats in Splats: Embedding Invisible 3D Watermark within Gaussian Splatting
3D Gaussian splatting (3DGS) has demonstrated impressive 3D reconstruction performance with explicit scene representations.
Collaborative AI in Sentiment Analysis: System Architecture, Data Prediction and Deployment Strategies
The advancement of large language model (LLM) based artificial intelligence technologies has been a game-changer, particularly in sentiment analysis.
Defense-as-a-Service: Black-box Shielding against Backdoored Graph Models
With the trend of large graph learning models, business owners tend to employ a model provided by a third party to deliver business services to users.
SFR-GNN: Simple and Fast Robust GNNs against Structural Attacks
Existing efforts are dedicated to purifying the maliciously modified structure or applying adaptive aggregation, thereby enhancing the robustness against adversarial structural attacks.
MemWarp: Discontinuity-Preserving Cardiac Registration with Memorized Anatomical Filters
In this paper, we address this issue with MemWarp, a learning framework that leverages a memory network to store prototypical information tailored to different anatomical regions.
OpticGAI: Generative AI-aided Deep Reinforcement Learning for Optical Networks Optimization
Furthermore, we assess the performance of OpticGAI on two NP-hard optical network problems, Routing and Wavelength Assignment (RWA) and dynamic Routing, Modulation, and Spectrum Allocation (RMSA), to show the feasibility of the AI-generated policy paradigm.
Graph Neural Backdoor: Fundamentals, Methodologies, Applications, and Future Directions
Graph Neural Networks (GNNs) have significantly advanced various downstream graph-relevant tasks, encompassing recommender systems, molecular structure prediction, social media analysis, etc.
Diffusion-based Reinforcement Learning for Dynamic UAV-assisted Vehicle Twins Migration in Vehicular Metaverses
In this paper, we propose a dynamic Unmanned Aerial Vehicle (UAV)-assisted VT migration framework in air-ground integrated networks, where UAVs act as aerial edge servers to assist ground RSUs during VT task offloading.
Multi-Agent RL-Based Industrial AIGC Service Offloading over Wireless Edge Networks
In this paper, we focus on the edge optimization of AIGC task execution and propose GMEL, a generative model-driven industrial AIGC collaborative edge learning framework.
Spikewhisper: Temporal Spike Backdoor Attacks on Federated Neuromorphic Learning over Low-power Devices
Federated neuromorphic learning (FedNL) leverages event-driven spiking neural networks and federated learning frameworks to effectively execute intelligent analysis tasks over amounts of distributed low-power devices but also perform vulnerability to poisoning attacks.
What Makes Good Collaborative Views? Contrastive Mutual Information Maximization for Multi-Agent Perception
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.
On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networks
Federated learning is a promising technique for effectively training GAI models in mobile edge networks due to its data distribution.
Adversarial Robustness of Link Sign Prediction in Signed Graphs
To address this limitation, we introduce Balance Augmented-Signed Graph Contrastive Learning (BA-SGCL), an innovative framework that combines contrastive learning with balance augmentation techniques to achieve robust graph representations.
Slicer Networks
In medical imaging, scans often reveal objects with varied contrasts but consistent internal intensities or textures.
Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learning
Researchers have struggled to design fair FL systems that ensure fairness of results.
Spatially Covariant Image Registration with Text Prompts
Medical images are often characterized by their structured anatomical representations and spatially inhomogeneous contrasts.
Ranked #2 on
Image Registration
on Unpaired-abdomen-CT
(using extra training data)
Privacy-preserving Few-shot Traffic Detection against Advanced Persistent Threats via Federated Meta Learning
Extensive experiments based on multiple benchmark datasets like CICIDS2017 and DAPT 2020 prove the superiority of proposed PFTD.
Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach
Graph anomaly detection (GAD) has achieved success and has been widely applied in various domains, such as fraud detection, cybersecurity, finance security, and biochemistry.
Privacy Inference-Empowered Stealthy Backdoor Attack on Federated Learning under Non-IID Scenarios
In this paper, we propose a novel privacy inference-empowered stealthy backdoor attack (PI-SBA) scheme for FL under non-IID scenarios.
Few-shot Multi-domain Knowledge Rearming for Context-aware Defence against Advanced Persistent Threats
Secondly, to rearm current security strategies, an finetuning-based deployment mechanism is proposed to transfer learned knowledge into the student model, while minimizing the defense cost.
CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications
Most existing studies on trustworthy FL aim to eliminate data poisoning threats that are produced by malicious clients, but in many cases, eliminating model poisoning attacks brought by fake servers is also an important objective.
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification
Graph Neural Networks (GNNs) are vulnerable to data poisoning attacks, which will generate a poisoned graph as the input to the GNN models.
Automatically Lock Your Neural Networks When You're Away
The smartphone and laptop can be unlocked by face or fingerprint recognition, while neural networks which confront numerous requests every day have little capability to distinguish between untrustworthy and credible users.
