Demo Data from EDBT 2026 / reviewers in the wild / expert
Van-Hoang Phan
dblp:410/6464
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
0009-0003-8615-435XORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Multimedia analysis and retrieval · 100% | |
| Artificial intelligence
1 paper |
Trustworthy machine learning · 50% Information extraction and text analysis · 50% |
Topics — the 4 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Multimedia analysis and retrieval › multimedia analysis › multimedia forensics
multimedia verification |
0.9 | 1 | 2025 | Fact-Checking at Scale: Multimodal AI for Authenticity and Context Verification in Online Media · ACM Multimedia 2025 |
Multimedia analysis and retrieval › harmful content detection › misinformation detection
multimodal fact-checking |
0.9 | 1 | 2025 | Fact-Checking at Scale: Multimodal AI for Authenticity and Context Verification in Online Media · ACM Multimedia 2025 |
Natural language and speech › Information extraction and text analysis
misinformation detection |
0.3 | 1 | 2025 | Fact-Checking at Scale: Multimodal AI for Authenticity and Context Verification in Online Media · ACM Multimedia 2025 |
Machine learning › Trustworthy machine learning
robustness |
0.3 | 1 | 2025 | Fact-Checking at Scale: Multimodal AI for Authenticity and Context Verification in Online Media · ACM Multimedia 2025 |
Methods — techniques the papers use, named apart from their topics
visual forensics · 1.7temporal alignment · 1.7semantic similarity · 1.7multimodal reasoning · 1.7geolocation · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Fact-Checking at Scale: Multimodal AI for Authenticity and Context Verification in Online MediaabstractThe proliferation of multimedia content on social media has transformed how information is produced and consumed, enabling real-time coverage of global events but also accelerating the spread of misinformation, particularly during crises such as wars, natural disasters, and elections. The rise of synthetic media and the reuse of authentic content in misleading contexts further underscore the need for robust verification tools. In this paper, we present a comprehensive system developed for the ACM Multimedia 2025 Grand Challenge on Multimedia Verification. Our system evaluates both the authenticity and contextual accuracy of multimedia content in multilingual settings, producing expert-oriented verification reports alongside accessible summaries for the public. We propose a unified verification pipeline that integrates visual forensics, textual analysis, and multimodal reasoning, with a hybrid approach to detecting out-of-context (OOC) media via semantic similarity, temporal alignment, and geolocation cues. Extensive evaluations on the challenge benchmark demonstrate the system's effectiveness across diverse real-world scenarios. Our contributions advance the state of the art in multimedia verification while providing practical tools for journalists, fact-checkers, and researchers addressing information integrity in the digital age. Van-Hoang Phan, Tung-Duong Le-Duc, Long-Khanh Pham, Anh-Thu Le, Quynh-Huong Dinh-Nguyen, Dang-Quan Vo, Hoang-Quoc Nguyen-Son, Anh-Duy Tran, Dang Vu, Minh-Son Dao |
ACM Multimedia | 1 |