# FakeSV-VLM: Taming VLM for Detecting Fake Short-Video News via Progressive Mixture-Of-Experts Adapter

arXiv: https://arxiv.org/abs/2508.19639 · HTML: https://mlsanity.com/paper/2508.19639/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal MoE models, one of 12 directions in Mixture-of-experts.
- Builds on 18 of its 44 references that are in the map.
- First posted on arXiv 27 Aug 2025.

## Placed in

- Mixture-of-experts / [Multimodal MoE models](https://mlsanity.com/moe/multimodal-moe-models/)

## Builds on (most cited first, 18 of 18)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [M2IV: Towards Efficient and Fine-grained Multimodal In-Context Learning via Representation Engineering](https://mlsanity.com/paper/2504.04633/) (2025)
- [Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding](https://mlsanity.com/paper/2502.06020/) (2025)
- [Disinformation Capabilities of Large Language Models](https://mlsanity.com/paper/2311.08838/) (2023)
- [TACO: Enhancing Multimodal In-context Learning via Task Mapping-Guided Sequence Configuration](https://mlsanity.com/paper/2505.17098/) (2025)
- [Two Heads Are Better Than One: Improving Fake News Video Detection by Correlating with Neighbors](https://mlsanity.com/paper/2306.05241/) (2023)
- [ASAP: Advancing Semantic Alignment Promotes Multi-Modal Manipulation Detecting and Grounding](https://mlsanity.com/paper/2412.12718/) (2024)
- [SoundMind: RL-Incentivized Logic Reasoning for Audio-Language Models](https://mlsanity.com/paper/2506.12935/) (2025)
- [The Coherence Trap: When MLLM-Crafted Narratives Exploit Manipulated Visual Contexts](https://mlsanity.com/paper/2505.17476/) (2025)
- [Generating Attribution Reports for Manipulated Facial Images: A Dataset and Baseline](https://mlsanity.com/paper/2412.19685/) (2024)
- [Consistency-aware Fake Videos Detection on Short Video Platforms](https://mlsanity.com/paper/2504.21495/) (2025)
- [Learning Sparsity for Effective and Efficient Music Performance Question Answering](https://mlsanity.com/paper/2506.01319/) (2025)
- [The Challenges of Studying Misinformation on Video-Sharing Platforms During Crises and Mass-Convergence Events](https://mlsanity.com/paper/2303.14309/) (2023)
- [A Survey on Watching Social Issue Videos among YouTube and TikTok Users](https://mlsanity.com/paper/2310.19193/) (2023)

## Built on by (most cited first, 7 of 7)

- [The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection](https://mlsanity.com/paper/2601.15316/) (2026)
- [Cultivating Forensic Reasoning for Generalizable Multimodal Manipulation Detection](https://mlsanity.com/paper/2603.01993/) (2026)
- [ProtoVQA: An Adaptable Prototypical Framework for Explainable Fine-Grained Visual Question Answering](https://mlsanity.com/paper/2509.16680/) (2025)
- [Perception, Understanding and Reasoning, A Multimodal Benchmark for Video Fake News Detection](https://mlsanity.com/paper/2510.24816/) (2025)
- [OmniVL-Guard Pro: A Tool-Augmented Agent for Omnibus Vision-Language Forensics](https://mlsanity.com/paper/2605.16962/) (2026)
- [Sparse Evidence Can Suffice: Agentic Evidence Seeking for Multimodal Video Misinformation Detection](https://mlsanity.com/paper/2607.18080/) (2026)
- [HyperClaim: Fine-Grained Cross-Modal Hypergraph Reasoning for Video Misinformation Detection](https://mlsanity.com/paper/2607.28375/) (2026)

## Abstract (arXiv)

We present FakeSV-VLM in this paper, a new VLM-based framework for detecting fake news on short video platforms. Despite significant efforts to combat this issue due to the severe threat that fake news videos pose to public information security, existing methods still fall short in detection accuracy, often due to lack of knowledge to verify the news is real or not. However, large Vision Language Models (VLMs) have absorbed extensive real-world knowledge from massive multimodal datasets. Motivated by this, we adapt advanced VLMs for fake news detection in short videos. Upon close examination of news samples, we observe that short video samples can be categorized into four distinct scenarios: both video and text are real (for real samples), or both are fake, or either the video or text is fake (for fake samples). Inspired by this insight, we design four experts tailored to handle each scenario and integrate them into VLM via Mixture of Experts. Specifically, we develop the Progressive MoE Adapter (PMOE) module where detection experts first provide an initial analysis, followed by attribution experts for a comprehensive diagnosis, leading to a robust decision. Additionally, we also note the fake news videos often show inconsistency between two modalities. Consequently, we further design the Alignment-driven Event Checking (ADEC) module, which perceives the fake news by capturing the inconsistency between different modalities. Extensive experiments on two benchmark datasets, FakeSV and FakeTT, verify the superiority of our model. It significantly outperforms current state-of-the-art models by +3.32% and +5.02%, establishing a new benchmark in the field.