Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Multimodal Fake News Detection: MFND Dataset and Shallow-Deep Multitask Learning
Authors: Ye Zhu, Yunan Wang, Zitong Yu
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both mainstream and our proposed datasets demonstrate the superiority of the model. The proposed SDML model achieves state-of-the-art detection and localization performance on four benchmark datasets under both multi-modal multi-task and multimodal single-task settings. |
| Researcher Affiliation | Academia | 1School of Artificial Intelligence, Hebei University of Technology 2School of Computing and Information Technology, Great Bay University 3Guangdong Provincial Key Laboratory of Intelligent Information Processing & Shenzhen Key Laboratory of Media Security, Shenzhen University 4Dongguan Key Laboratory for Intelligence and Information Technology |
| Pseudocode | No | The paper describes its methodology through textual explanations and a conceptual diagram (Figure 3), but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code, nor does it provide any links to a code repository. |
| Open Datasets | Yes | We present a new Multimodal Fake News Detection dataset (MFND) containing 11 manipulated types, designed to detect and localize highly authentic fake news. ... We contribute the MFND dataset which uses 11 state-of-the-art image and text manipulation methods and provides rich detection and localization labels that fit a wide range of realistic scenarios. |
| Dataset Splits | Yes | The dataset is divided into three parts, the 95k pairs are part of model training, another 15k pairs are part of testing and the remaining 15k are for testing. |
| Hardware Specification | No | The computational resources are supported by Song Shan Lake HPC Center (SSL-HPC) in Great Bay University. This statement indicates the source of computational resources but does not specify particular hardware components like CPU or GPU models. |
| Software Dependencies | No | All experiments are implemented on the Pytorch deep learning framework. The paper mentions PyTorch but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | The momentum queue size is set to 65535, the media detection, image detection, image localization, and text detection projections are set to three different multi-layer perceptual with output dimensions of 2, 2, 4, and 2. The model trained for 100 epochs with a batch size of 64, Adam W optimizer, with a weight decay of 0.005, in the first 1000 steps the learning rate is warmed up to 5e 6, decaying to 5e 7 after the cosine scheduling. |