Paper
EA-Swin: An Embedding-Agnostic Swin Transformer for AI-Generated Video Detection
Authors
Hung Mai, Loi Dinh, Duc Hai Nguyen, Dat Do, Luong Doan, Khanh Nguyen Quoc, Huan Vu, Phong Ho, Naeem Ul Islam, Tuan Do
Abstract
Recent advances in foundation video generators such as Sora2, Veo3, and other commercial systems have produced highly realistic synthetic videos, exposing the limitations of existing detection methods that rely on shallow embedding trajectories, image-based adaptation, or computationally heavy MLLMs. We propose EA-Swin, an Embedding-Agnostic Swin Transformer that models spatiotemporal dependencies directly on pretrained video embeddings via a factorized windowed attention design, making it compatible with generic ViT-style patch-based encoders. Alongside the model, we construct the EA-Video dataset, a benchmark dataset comprising 130K videos that integrates newly collected samples with curated existing datasets, covering diverse commercial and open-source generators and including unseen-generator splits for rigorous cross-distribution evaluation. Extensive experiments show that EA-Swin achieves 0.97-0.99 accuracy across major generators, outperforming prior SoTA methods (typically 0.8-0.9) by a margin of 5-20%, while maintaining strong generalization to unseen distributions, establishing a scalable and robust solution for modern AI-generated video detection.
Metadata
Related papers
Vibe Coding XR: Accelerating AI + XR Prototyping with XR Blocks and Gemini
Ruofei Du, Benjamin Hersh, David Li, Nels Numan, Xun Qian, Yanhe Chen, Zhongy... • 2026-03-25
Comparing Developer and LLM Biases in Code Evaluation
Aditya Mittal, Ryan Shar, Zichu Wu, Shyam Agarwal, Tongshuang Wu, Chris Donah... • 2026-03-25
The Stochastic Gap: A Markovian Framework for Pre-Deployment Reliability and Oversight-Cost Auditing in Agentic Artificial Intelligence
Biplab Pal, Santanu Bhattacharya • 2026-03-25
Retrieval Improvements Do Not Guarantee Better Answers: A Study of RAG for AI Policy QA
Saahil Mathur, Ryan David Rittner, Vedant Ajit Thakur, Daniel Stuart Schiff, ... • 2026-03-25
MARCH: Multi-Agent Reinforced Self-Check for LLM Hallucination
Zhuo Li, Yupeng Zhang, Pengyu Cheng, Jiajun Song, Mengyu Zhou, Hao Li, Shujie... • 2026-03-25
Raw Data (Debug)
{
"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.17260v1</id>\n <title>EA-Swin: An Embedding-Agnostic Swin Transformer for AI-Generated Video Detection</title>\n <updated>2026-02-19T11:04:20Z</updated>\n <link href='https://arxiv.org/abs/2602.17260v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.17260v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Recent advances in foundation video generators such as Sora2, Veo3, and other commercial systems have produced highly realistic synthetic videos, exposing the limitations of existing detection methods that rely on shallow embedding trajectories, image-based adaptation, or computationally heavy MLLMs. We propose EA-Swin, an Embedding-Agnostic Swin Transformer that models spatiotemporal dependencies directly on pretrained video embeddings via a factorized windowed attention design, making it compatible with generic ViT-style patch-based encoders. Alongside the model, we construct the EA-Video dataset, a benchmark dataset comprising 130K videos that integrates newly collected samples with curated existing datasets, covering diverse commercial and open-source generators and including unseen-generator splits for rigorous cross-distribution evaluation. Extensive experiments show that EA-Swin achieves 0.97-0.99 accuracy across major generators, outperforming prior SoTA methods (typically 0.8-0.9) by a margin of 5-20%, while maintaining strong generalization to unseen distributions, establishing a scalable and robust solution for modern AI-generated video detection.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CV'/>\n <published>2026-02-19T11:04:20Z</published>\n <arxiv:comment>First preprint</arxiv:comment>\n <arxiv:primary_category term='cs.CV'/>\n <author>\n <name>Hung Mai</name>\n </author>\n <author>\n <name>Loi Dinh</name>\n </author>\n <author>\n <name>Duc Hai Nguyen</name>\n </author>\n <author>\n <name>Dat Do</name>\n </author>\n <author>\n <name>Luong Doan</name>\n </author>\n <author>\n <name>Khanh Nguyen Quoc</name>\n </author>\n <author>\n <name>Huan Vu</name>\n </author>\n <author>\n <name>Phong Ho</name>\n </author>\n <author>\n <name>Naeem Ul Islam</name>\n </author>\n <author>\n <name>Tuan Do</name>\n </author>\n </entry>"
}