14 accepted papers
Privacy concerns have been raised in Large Language Models (LLM) inference when models are deployed in Cloud Service Providers (CSP). Homomorphic encryption (HE) offers a promising solution by enabling secure inference directly over encrypted inputs. However, the high computational overhead of HE re…
Point cloud-based motion capture leverages rich spatial geometry and privacy-preserving sensing, but learning robust representations from noisy, unstructured point clouds remains challenging. Existing approaches face a struggle trade-off between point-based methods (geometrically detailed but noisy)…
Parameter-efficient fine-tuning (PEFT) aims to adapt large pretrained speech models using fewer trainable parameters while maintaining performance. Low-Rank Adaptation (LoRA) achieves this by decomposing weight updates into two low-rank matrices, $A$ and $B$, such that $W'=W_0+BA$. Previous studies…
The filter bubble is a notorious issue in Recommender Systems (RSs), characterized by users being confined to a limited corpus of information or content that strengthens and amplifies their pre-established preferences and beliefs. Most existing methods primarily aim to analyze filter bubbles in the…
To enhance the reliability and credibility of graph neural networks (GNNs) and improve the transparency of their decision logic, a new field of explainability of GNNs (XGNN) has emerged. However, two major limitations severely degrade the performance and hinder the generalizability of existing XGNN
Unfairness is a well-known challenge in Recommender Systems (RSs), often resulting in biased outcomes that disadvantage users or items based on attributes such as gender, race, age, or popularity. Although some approaches have started to improve fairness recommendation in offline or static contexts,…
Most face anti-spoofing methods address the generalization problem by extracting domain-invariant representations from multiple source domains or unlabelled target data. However, their deployment in real-world applications is unfeasible when data is insufficient or unavailable due to the collection…
The Matthew effect is a big challenge in Recommender Systems (RSs), where popular items tend to receive increasing attention, while less popular ones are often overlooked, perpetuating existing disparities. Although many existing methods attempt to mitigate Matthew effect in the static or quasi-stat…
Extensive studies have demonstrated that deep neural networks (DNNs) are vulnerable to adversarial examples (AEs), which brings a huge security risk to the application of DNNs, especially for the AI models developed in the real world. To impede the process of fully exploiting the vulnerabilities of…
"Skeleton Action Recognition (SAR) has attracted significant interest for its efficient representation of the human skeletal structure. Despite its advancements, recent studies have raised security concerns in SAR models, particularly their vulnerability to adversarial attacks. However, such strateg…
Knowledge Graph Completion (KGC) often requires both KG structural and textual information to be effective. Pre-trained Language Models (PLMs) have been used to learn the textual information, usually under the fine-tune paradigm for the KGC task. However, the fine-tuned PLMs often overwhelmingly foc…
Knowledge Graph Completion (KGC) has been recently extended to multiple knowledge graph (KG) structures, initiating new research directions, e.g. static KGC, temporal KGC and few-shot KGC. Previous works often design KGC models closely coupled with specific graph structures, which inevitably results…
The introduction of non-local blocks to the traditional CNN architecture enhances its performance for various computer vision tasks by improving its capabilities of capturing long range dependencies. However, the usage of non-local blocks may also introduce new threats to computer vision systems. Th…
Presentation attack is one of the main threats to face verification systems and attracts great attention of research community. Recent methods achieve great success in intra-database test. However, the problem is more complex in practical scenario as the type of attack could be unseen to system desi…