Vision Graph Non-Contrastive Learning for Audio Deepfake Detection with Limited Labels

Recent advancements in audio deepfake detection have leveraged graph neural networks (GNNs) to model frequency and temporal interdependencies in audio data, effectively identifying deepfake artifacts. However, the reliance of GNN-based methods on substantial labeled data for graph construction and robust performance limits their applicability in scenarios with limited labeled data. Although vast amounts of audio data exist, the process of labeling samples as genuine or fake remains labor-intensive and costly. To address this challenge, we propose SIGNL (Spatio-temporal vIsion Graph Non-contrastive Learning), a novel framework that maintains high GNN performance in low-label settings. SIGNL constructs spatio-temporal graphs by representing patches from the audio's visual spectrogram as nodes. These graph structures are modeled using vision graph convolutional (GC) encoders pre-trained through graph non-contrastive learning, a label-free that maximizes the similarity between positive pairs. The pre-trained encoders are then fine-tuned for audio deepfake detection, reducing reliance on labeled data. Experiments demonstrate that SIGNL outperforms state-of-the-art baselines across multiple audio deepfake detection datasets, achieving the lowest Equal Error Rate (EER) with as little as 5% labeled data. Additionally, SIGNL exhibits strong cross-domain generalization, achieving the lowest EER in evaluations involving diverse attack types and languages in the In-The-Wild dataset.

GraphDART: Graph Distillation for Efficient Advanced Persistent Threat Detection

Cyber-physical-social systems (CPSSs) have emerged in many applications over recent decades, requiring increased attention to security concerns. The rise of sophisticated threats like Advanced Persistent Threats (APTs) makes ensuring security in CPSSs particularly challenging. Provenance graph analysis has proven effective for tracing and detecting anomalies within systems, but the sheer size and complexity of these graphs hinder the efficiency of existing methods, especially those relying on graph neural networks (GNNs). To address these challenges, we present GraphDART, a modular framework designed to distill provenance graphs into compact yet informative representations, enabling scalable and effective anomaly detection. GraphDART can take advantage of diverse graph distillation techniques, including classic and modern graph distillation methods, to condense large provenance graphs while preserving essential structural and contextual information. This approach significantly reduces computational overhead, allowing GNNs to learn from distilled graphs efficiently and enhance detection performance. Extensive evaluations on benchmark datasets demonstrate the robustness of GraphDART in detecting malicious activities across cyber-physical-social systems. By optimizing computational efficiency, GraphDART provides a scalable and practical solution to safeguard interconnected environments against APTs.

Balanced Graph Structure Information for Brain Disease Detection

Analyzing connections between brain regions of interest (ROI) is vital to detect neurological disorders such as autism or schizophrenia. Recent advancements employ graph neural networks (GNNs) to utilize graph structures in brains, improving detection performances. Current methods use correlation measures between ROI's blood-oxygen-level-dependent (BOLD) signals to generate the graph structure. Other methods use the training samples to learn the optimal graph structure through end-to-end learning. However, implementing those methods independently leads to some issues with noisy data for the correlation graphs and overfitting problems for the optimal graph. In this work, we proposed Bargrain (balanced graph structure for brains), which models two graph structures: filtered correlation matrix and optimal sample graph using graph convolution networks (GCNs). This approach aims to get advantages from both graphs and address the limitations of only relying on a single type of structure. Based on our extensive experiment, Bargrain outperforms state-of-the-art methods in classification tasks on brain disease datasets, as measured by average F1 scores.

Entropy Causal Graphs for Multivariate Time Series Anomaly Detection

Many multivariate time series anomaly detection frameworks have been proposed and widely applied. However, most of these frameworks do not consider intrinsic relationships between variables in multivariate time series data, thus ignoring the causal relationship among variables and degrading anomaly detection performance. This work proposes a novel framework called CGAD, an entropy Causal Graph for multivariate time series Anomaly Detection. CGAD utilizes transfer entropy to construct graph structures that unveil the underlying causal relationships among time series data. Weighted graph convolutional networks combined with causal convolutions are employed to model both the causal graph structures and the temporal patterns within multivariate time series data. Furthermore, CGAD applies anomaly scoring, leveraging median absolute deviation-based normalization to improve the robustness of the anomaly identification process. Extensive experiments demonstrate that CGAD outperforms state-of-the-art methods on real-world datasets with a 15% average improvement based on three different multivariate time series anomaly detection metrics.

Graph Lifelong Learning: A Survey

Graph learning substantially contributes to solving artificial intelligence (AI) tasks in various graph-related domains such as social networks, biological networks, recommender systems, and computer vision. However, despite its unprecedented prevalence, addressing the dynamic evolution of graph data over time remains a challenge. In many real-world applications, graph data continuously evolves. Current graph learning methods that assume graph representation is complete before the training process begins are not applicable in this setting. This challenge in graph learning motivates the development of a continuous learning process called graph lifelong learning to accommodate the future and refine the previous knowledge in graph data. Unlike existing survey papers that focus on either lifelong learning or graph learning separately, this survey paper covers the motivations, potentials, state-of-the-art approaches (that are well categorized), and open issues of graph lifelong learning. We expect extensive research and development interest in this emerging field.