EA-Swin: An Embedding-Agnostic Swin Transformer for AI-Generated Video Detection

2026-02-19

AI Summary

This work addresses the challenge of detecting AI-generated videos from high-fidelity models such as Sora2 and Veo3, which current methods struggle to handle due to their reliance on shallow features or computationally expensive multimodal architectures. We propose EA-Swin, an embedding-agnostic Swin Transformer that leverages a factorized window attention mechanism to directly model spatiotemporal dependencies in pretrained video embeddings, offering compatibility with any Vision Transformer (ViT)-style encoder. To support comprehensive evaluation, we introduce EA-Video, a new benchmark comprising 130,000 videos, enabling the first unified assessment of generalization across diverse ViT-based embeddings and unseen generators. EA-Swin achieves state-of-the-art accuracy of 0.97–0.99 on mainstream generators, outperforming existing methods by 5%–20%, and demonstrates strong generalization to previously unseen video synthesis models.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: AI for Accessibility

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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.

Problem

• AI-generated video detection
• deepfake detection
• video forensics
• synthetic video
• foundation video generators

Innovation

• Embedding-Agnostic
• Swin Transformer
• Spatiotemporal Modeling
• AI-Generated Video Detection
• Factorized Windowed Attention