KGAlign: Joint Semantic-Structural Knowledge Encoding for Multimodal Fake News Detection

• URL: http://arxiv.org/abs/2505.14714v1
• Date: Sun, 18 May 2025 13:08:38 GMT
• Title: KGAlign: Joint Semantic-Structural Knowledge Encoding for Multimodal Fake News Detection
• Authors: Tuan-Vinh La, Minh-Hieu Nguyen, Minh-Son Dao,
• Abstract summary: We propose a novel multi-modal fake news detection framework that integrates visual, textual, and knowledge-based representations.<n>Our proposal introduces a new paradigm: knowledge-grounded multimodal reasoning.
• Score: 2.3047429933576327
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fake news detection remains a challenging problem due to the complex interplay between textual misinformation, manipulated images, and external knowledge reasoning. While existing approaches have achieved notable results in verifying veracity and cross-modal consistency, two key challenges persist: (1) Existing methods often consider only the global image context while neglecting local object-level details, and (2) they fail to incorporate external knowledge and entity relationships for deeper semantic understanding. To address these challenges, we propose a novel multi-modal fake news detection framework that integrates visual, textual, and knowledge-based representations. Our approach leverages bottom-up attention to capture fine-grained object details, CLIP for global image semantics, and RoBERTa for context-aware text encoding. We further enhance knowledge utilization by retrieving and adaptively selecting relevant entities from a knowledge graph. The fused multi-modal features are processed through a Transformer-based classifier to predict news veracity. Experimental results demonstrate that our model outperforms recent approaches, showcasing the effectiveness of neighbor selection mechanism and multi-modal fusion for fake news detection. Our proposal introduces a new paradigm: knowledge-grounded multimodal reasoning. By integrating explicit entity-level selection and NLI-guided filtering, we shift fake news detection from feature fusion to semantically grounded verification. For reproducibility and further research, the source code is publicly at \href{https://github.com/latuanvinh1998/KGAlign}{github.com/latuanvinh1998/KGAlign}.

Related papers

• Multimodal Fake News Detection: MFND Dataset and Shallow-Deep Multitask Learning [22.494473679788396]
  Multimodal news contains a wealth of information and is easily affected by deepfake modeling attacks.<n>To combat the latest image and text generation methods, we present a new Multimodal Fake News Detection dataset (MFND)<n>MFND contains 11 manipulated types, designed to detect and localize highly authentic fake news.
  arXiv  Detail & Related papers  (2025-05-11T00:26:13Z)
• Dynamic Analysis and Adaptive Discriminator for Fake News Detection [59.41431561403343]
  We propose a Dynamic Analysis and Adaptive Discriminator (DAAD) approach for fake news detection.<n>For knowledge-based methods, we introduce the Monte Carlo Tree Search algorithm to leverage the self-reflective capabilities of large language models.<n>For semantic-based methods, we define four typical deceit patterns to reveal the mechanisms behind fake news creation.
  arXiv  Detail & Related papers  (2024-08-20T14:13:54Z)
• FKA-Owl: Advancing Multimodal Fake News Detection through Knowledge-Augmented LVLMs [48.32113486904612]
  We propose FKA-Owl, a framework that leverages forgery-specific knowledge to augment Large Vision-Language Models (LVLMs) Experiments on the public benchmark demonstrate that FKA-Owl achieves superior cross-domain performance compared to previous methods.
  arXiv  Detail & Related papers  (2024-03-04T12:35:09Z)
• MSynFD: Multi-hop Syntax aware Fake News Detection [27.046529059563863]
  Social media platforms have fueled the rapid dissemination of fake news, posing threats to our real-life society. Existing methods use multimodal data or contextual information to enhance the detection of fake news. We propose a novel multi-hop syntax aware fake news detection (MSynFD) method, which incorporates complementary syntax information to deal with subtle twists in fake news.
  arXiv  Detail & Related papers  (2024-02-18T05:40:33Z)
• From Text to Pixels: A Context-Aware Semantic Synergy Solution for Infrared and Visible Image Fusion [66.33467192279514]
  We introduce a text-guided multi-modality image fusion method that leverages the high-level semantics from textual descriptions to integrate semantics from infrared and visible images. Our method not only produces visually superior fusion results but also achieves a higher detection mAP over existing methods, achieving state-of-the-art results.
  arXiv  Detail & Related papers  (2023-12-31T08:13:47Z)
• Exploring Multi-Modal Contextual Knowledge for Open-Vocabulary Object Detection [72.36017150922504]
  We propose a multi-modal contextual knowledge distillation framework, MMC-Det, to transfer the learned contextual knowledge from a teacher fusion transformer to a student detector. The diverse multi-modal masked language modeling is realized by an object divergence constraint upon traditional multi-modal masked language modeling (MLM)
  arXiv  Detail & Related papers  (2023-08-30T08:33:13Z)
• ESTextSpotter: Towards Better Scene Text Spotting with Explicit Synergy in Transformer [88.61312640540902]
  We introduce Explicit Synergy-based Text Spotting Transformer framework (ESTextSpotter) Our model achieves explicit synergy by modeling discriminative and interactive features for text detection and recognition within a single decoder. Experimental results demonstrate that our model significantly outperforms previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-08-20T03:22:23Z)
• Towards General Visual-Linguistic Face Forgery Detection [95.73987327101143]
  Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust. Existing methods mostly treat this task as binary classification, which uses digital labels or mask signals to train the detection model. We propose a novel paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses fine-grained sentence-level prompts as the annotation.
  arXiv  Detail & Related papers  (2023-07-31T10:22:33Z)
• Multi-modal Fake News Detection on Social Media via Multi-grained Information Fusion [21.042970740577648]
  We present a Multi-grained Multi-modal Fusion Network (MMFN) for fake news detection. Inspired by the multi-grained process of human assessment of news authenticity, we respectively employ two Transformer-based pre-trained models to encode token-level features from text and images. The multi-modal module fuses fine-grained features, taking into account coarse-grained features encoded by the CLIP encoder.
  arXiv  Detail & Related papers  (2023-04-03T09:13:59Z)
• Contextual information integration for stance detection via cross-attention [59.662413798388485]
  Stance detection deals with identifying an author's stance towards a target. Most existing stance detection models are limited because they do not consider relevant contextual information. We propose an approach to integrate contextual information as text.
  arXiv  Detail & Related papers  (2022-11-03T15:04:29Z)
• Multimodal Fake News Detection with Adaptive Unimodal Representation Aggregation [28.564442206829625]
  AURA is a multimodal fake news detection network with adaptive unimodal representation aggregation. We perform coarse-level fake news detection and cross-modal cosistency learning according to the unimodal and multimodal representations. Experiments on Weibo and Gossipcop prove that AURA can successfully beat several state-of-the-art FND schemes.
  arXiv  Detail & Related papers  (2022-06-12T14:06:55Z)
• Applying Automatic Text Summarization for Fake News Detection [4.2177790395417745]
  The distribution of fake news is not a new but a rapidly growing problem. We present an approach to the problem that combines the power of transformer-based language models. Our framework, CMTR-BERT, combines multiple text representations and enables the incorporation of contextual information.
  arXiv  Detail & Related papers  (2022-04-04T21:00:55Z)
• Visual Relationship Detection with Visual-Linguistic Knowledge from Multimodal Representations [103.00383924074585]
  Visual relationship detection aims to reason over relationships among salient objects in images. We propose a novel approach named Visual-Linguistic Representations from Transformers (RVL-BERT) RVL-BERT performs spatial reasoning with both visual and language commonsense knowledge learned via self-supervised pre-training.
  arXiv  Detail & Related papers  (2020-09-10T16:15:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.