Fact-checking, also referred to as fact verification, is essential for evaluating the accuracy of claims and curbing the dissemination and influence of misinformation. Recent advancements in Large Language Models (LLMs) have enabled their use in automated fact-checking systems. These approaches frequently adopt prompting techniques within a ''divide-and-conquer'' framework, where complex claims are broken down into simpler sub-claims that are individually verified to reduce the overall complexity of the task. These existing works often neglect the dependency between sub-claims and verify them in isolation. For complex claims, particularly those requiring multi-hop reasoning, the interconnections between sub-claims are crucial, as verifying each one independently often fails to capture the full context and reasoning needed for accurate verification. To address this, we propose DagFC, a novel LLM-based framework that performs Dependency-Aware Task Generation, Scheduling and Processing for Fact-Checking. DagFC constructs Knowledge Graphs (KGs) from claims to guide the decomposition of fact-checking problems and build dependent verification sub-tasks that capture the interrelations between sub-claims. This dependency-aware approach ensures more coherent and accurate verification by integrating intermediate results. Additionally, DagFC leverages LLMs throughout the verification process, from KG construction to final veracity prediction, enhancing reasoning and generation capabilities. Extensive experiments on two benchmark datasets, FEVEROUS and HoVer, demonstrate that DagFC outperforms state-of-the-art methods in both accuracy and Macro-F1 score. Furthermore, we present a user-friendly fact-checking prototype based on our framework, offering practical value for both research and public use.

The rapid spread of fake news on social media has caused great harm to society in recent years, which raises the detection of fake news as an urgent task. Recent methods utilize the interactions among different entities such as authors, subjects, and news articles to model news propagation as a static heterogeneous information network (HIN). However, this is suboptimal since fake news emerges dynamically, and the latent chronological interactions between news in HIN are essential signals for fake news detection. To this end, we model the dynamics of news and associated entities as a News-Driven Dynamic Heterogeneous Information Network (News-DyHIN), where the temporal relationships among news articles are well captured with meta-path based temporal neighbors. With the support of News-DyHIN, we propose a novel fake news detection framework, named D ynam i c H ierarchical A ttention N etwork (DiHAN), which learns news representations via a hierarchical attention mechanism to fuse temporal interactions among news articles. In particular, DiHAN first employs a temporal node level attention to learn the temporal information from meta-path based news neighbors through the modeled News-DyHIN. Then, a semantic attention layer is adopted to fuse different types of meta-path based temporal information for news representation learning. Extensive evaluations conducted on two public real-world datasets demonstrate that our proposed DiHAN achieves significant improvements over established baseline models.

Deepfakes have gained widespread attention as they may give rise to a series of risks ranging from personal reputation damages to national security breaches. A mainstream approach to generating deepfakes is based on Generative Adversarial Networks (GAN). Various methods have been proposed to detect GAN-generated fake content. However, most of them only target a specific GAN and do not generalize well to other unseen GAN architectures. Moreover, many existing methods show poor performance on deepfakes that are imperceptible to the human eye as they heavily rely on visual artifacts, such as unblinking eyes and asymmetric faces. In this work, we exploit the spectral artifacts left by up-sampling operations that are universally used in GAN architectures for detecting high-fidelity deepfake videos. We first divide video frames into blocks containing the most informative areas, e.g., face, eyes, and mouth areas, and use their spectrum to train a ResNet-based classifier to detect GAN-generated images. Experimental results on public datasets show that our method is effective in detecting high-fidelity deepfakes and generalizes well across GANs with the same or similar up-sampling operations.