Enhancing Bangla Fake News Detection Using Bidirectional Gated Recurrent Units and Deep Learning Techniques
The study aims to address the challenges of Bangla fake news detection using deep learning models, including the bidirectional Gated Recurrent Unit (GRU), and achieve high accuracy in identifying authentic and fake information.
FakeNewsGPT4: Advancing Multimodal Fake News Detection through Knowledge-Augmented LVLMs
FakeNewsGPT4 utilizes LVLMs and forgery-specific knowledge to improve cross-domain performance in detecting multimodal fake news.
TT-BLIP: Enhancing Fake News Detection Using BLIP and Tri-Transformer
TT-BLIP introduces a novel model for fake news detection by integrating text, image, and image-text features using advanced fusion techniques.
Enhancing Fake News Detection Using TT-BLIP and Tri-Transformer
TT-BLIP model outperforms state-of-the-art models in fake news detection by integrating text, image, and multimodal features.
DPOD: Domain-Specific Prompt Tuning for Multimodal Fake News Detection
異なるドメインのデータを活用して、偽ニュースの検出を改善するための新しいフレームワークであるDPODが提案されました。
FakeNewsGPT4: Advancing Multimodal Fake News Detection through Knowledge-Augmented LVLMs
LVLMs augmented with forgery-specific knowledge improve cross-domain performance in multimodal fake news detection.
Advancing Multimodal Fake News Detection with FakeNewsGPT4
FakeNewsGPT4 proposes a novel framework that leverages world knowledge from LVLMs and forgery-specific knowledge to enhance multimodal fake news detection.
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