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

Hung Mai; Loi Dinh; Duc Hai Nguyen; Dat Do; Luong Doan; Khanh Nguyen Quoc; Huan Vu; Naeem Ul Islam

cs.CVFeb 19, 2026

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, Naeem Ul Islam, +1 more

Organizations: N2TP Technology Solution JSC, Hanoi, Vietnam · College of Technology, National Economics University, Hanoi, Vietnam · University of Science, Vietnam National University, Ho Chi Minh City, Vietnam · Faculty of AI and Data Science, Phenikaa University, Hanoi, Vietnam · The Saigon International University, Ho Chi Minh City, Vietnam · College of Informatics, Yuan Ze University, Taoyuan, Taiwan

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. Moreover, 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.

Figures & tables

Figure 1: Sampled video frames from some AI video generators. Top: Recent generators produce high-quality visuals and realistic motion, closely resembling real videos. Bottom: Earlier models show clear artifacts, distorted content, and unnatural motion.

Figure 2: Real video data and AI videod data portion by Generators and Sources.

Figure 3: Train/Validation/Test set split.

Figure 4: t-SNE visualization of embedding trajectories. Each polyline represents the temporal evolution of video embeddings from V-JEPA 2 encoder.

Figure 5: Spatiotemporal window shifting mechanism. The input video embedding is first partitioned into non-overlapping local windows (1) along spatial and temporal dimensions. To enable cross-window interaction and enhance global context modeling, the windows are then shifted (2) spatially across adjacent regions and temporally across neighboring frames.

Model	Metric	Veo3	Sora2	HY	CVX	EA	LTX	Pika1	Wan2	Kling2	Sora	Avg
	Acc	0.511	0.510	0.512	0.511	0.512	0.511	0.511	0.511	0.506	0.511	0.5105
	Recall	1.000	1.000	1.000	1.000	1.000	1.000	1.000	0.999	0.993	1.000	0.9991
	F1	0.676	0.675	0.677	0.676	0.677	0.676	0.676	0.676	0.671	0.676	0.6757
D3	AUC	0.374	0.671	0.190	0.304	0.168	0.366	0.264	0.573	0.548	0.319	0.3778
	Acc	0.683	0.641	0.641	0.723	0.736	0.725	0.726	0.698	0.655	0.646	0.6874
	Recall	0.890	0.802	0.986	0.956	0.988	0.963	0.968	0.903	0.810	0.837	0.9103

Table 1: Benchmark results on the evaluation (seen) set , grouped by video generator. For each generator in the test set, the number of real videos is approximately balanced with the number of AI-generated videos from that generator. Abbreviation: HY (Hunyuan), CVX (CogVideoX), EA (EasyAnimate)

Model	Metric	Unk	RM2	Kling	Hailuo	SD	Mochi	JM	Gen3	Luma	Vidu	PRM	SKR	PV	Pika2	Gen4	Avg
	Acc	0.511	0.511	0.512	0.510	0.512	0.515	0.512	0.512	0.513	0.512	0.511	0.519	0.515	0.514	0.545	0.515
	Recall	1.000	1.000	1.000	0.997	1.000	0.998	0.998	1.000	1.000	1.000	1.000	0.992	1.000	1.000	0.981	0.998
	F1	0.676	0.676	0.677	0.675	0.677	0.678	0.676	0.677	0.678	0.677	0.676	0.678	0.678	0.678	0.688	0.678
D3	AUC	0.507	0.321	0.281	0.572	0.449	0.467	0.293	0.389	0.246	0.347	0.290	0.411	0.447	0.544	0.550	0.408
	Acc	0.628	0.699	0.718	0.654	0.619	0.638	0.703	0.599	0.726	0.698	0.713	0.722	0.701	0.676	0.701	0.680
	Recall	0.774	0.901	0.928	0.791	0.741	0.822	0.940	0.696	0.956	0.925	0.943	0.925	0.917	0.914	0.883	0.870

Table 2: Benchmark results on the test (unseen) set , grouped by video generator. Abbreviation: RM2 (Realmotion2), SD (SeeDance), JM (Jimeng), PRM (PyramidFlow), SKR (SkyReels), PV (PixVerse)

Figure 9: Model architecture ablations experiment result on test set.

	Base	Blur	Comp.	Noise
Acc	0.974	0.955	0.931	0.916
Prec	0.983	0.969	0.976	0.994
Recall	0.965	0.942	0.938	0.841
F1	0.974	0.955	0.956	0.912
AUC	0.997	0.991	0.990	0.988

Table 4: Robustness test for EA-Swin on the validation set

Appendix figures & tables15 assets

Supplementary material from the paper’s appendix.

Appendix

Split	Generator	#AI_vids	%in_split	#correspond_real	Published
Train set
train	veo3	5054	13.691	-	7/2025
train	sora2	4857	13.158	-	9/2025
train	cogvideox	4605	12.475	-	3/2025
train	hunyuan	4524	12.256	-	3/2025
train	easyanimate	4199	11.375	-	7/2024

Appendix

Table 5: Dataset composition per generator and split

Category	Setting	Value
Optimizer	Optimizer	AdamW
	Learning rate	3e-4
	Weight decay	0.05
	LR schedule	Cosine decay
	Warmup	1 epoch
	Min learning rate	1e-6

Appendix

Table 6: Base training config detail

Model	Configs	Metric	Veo3	Sora2	HY	CVX	EA	LTX	Pika1	Wan2	Kling2	Sora
Model Dimension
Dimension reduction	–d_model 256 –depth_t 2 –depth_s 2	Acc	0.976	0.977	0.988	0.986	0.990	0.988	0.985	0.985	0.987	0.985
		Prec	0.986	0.984	0.983	0.986	0.984	0.982	0.986	0.981	0.988	0.988
		Recall	0.967	0.971	0.994	0.988	0.997	0.995	0.986	0.989	0.987	0.983
		F1	0.977	0.978	0.989	0.987	0.990	0.988	0.986	0.985	0.987	0.985
		AUC	0.997	0.997	0.999	0.999	1.000	1.000	0.999	0.999	0.999	0.998

Appendix

Table 7: Hyperparameter sweep result on val set

Model	Configs	Metric	Unk	RM2	Kling1	Hailuo	SD	Mochi	JM	Gen3	Luma	Vidu	PRM	SKR	PV	Pika2	Gen4
Model Dimension
Dimension reduction	–d_model 256 –depth_t 2 –depth_s 2	Acc	0.953	0.987	0.952	0.970	0.983	0.910	0.991	0.986	0.971	0.982	0.982	0.963	0.978	0.967	0.934
		Prec	0.981	0.985	0.985	0.986	0.982	0.984	0.986	0.980	0.992	0.970	0.980	0.982	0.975	0.983	1.000
		Recall	0.925	0.989	0.921	0.956	0.986	0.838	0.996	0.992	0.952	0.996	0.986	0.945	0.982	0.952	0.870
		F1	0.952	0.987	0.952	0.971	0.984	0.905	0.991	0.986	0.971	0.983	0.983	0.963	0.978	0.967	0.931
		AUC	0.991	0.999	0.993	0.996	0.998	0.984	0.998	0.998	0.998	0.999	0.999	0.995	0.998	0.997	0.997

Appendix

Table 8: Parameter sweep result on test set

Model	Metric	Veo3	Sora2	HY	CVX	EA	LTX	Pika1	Wan2	Kling2	Sora	Avg
Base	Acc	0.984	0.982	0.989	0.986	0.991	0.987	0.989	0.985	0.988	0.985	0.999
	Prec	0.985	0.980	0.981	0.981	0.983	0.978	0.989	0.978	0.987	0.985	0.987
	Recall	0.982	0.984	0.997	0.992	0.999	0.997	0.990	0.993	0.990	0.986	0.983
	F1	0.984	0.982	0.989	0.987	0.991	0.988	0.989	0.986	0.989	0.986	0.991
	AUC	0.998	0.998	1.000	0.999	1.000	1.000	0.999	0.999	0.999	0.997	0.987
Ablation 1	Acc	0.976	0.981	0.981	0.984	0.990	0.990	0.988	0.991	0.990	0.985	0.986

Appendix

Table 9: Ablation of VJEPA-2 val set

Model	Metric	Unk	RM2	Kling1	Hailuo	SD	Mochi	JM	Gen3	Luma	Vidu	PRM	SKR	PV	Pika2	Gen4	Avg
Base	Acc	0.961	0.987	0.959	0.980	0.983	0.920	0.992	0.985	0.970	0.979	0.988	0.976	0.982	0.975	0.967	0.974
	Prec	0.980	0.985	0.984	0.984	0.986	0.986	0.986	0.980	0.990	0.968	0.988	0.980	0.972	0.984	0.993	0.983
	Recall	0.943	0.989	0.936	0.977	0.981	0.855	0.998	0.990	0.952	0.992	0.990	0.972	0.993	0.968	0.942	0.965
	F1	0.961	0.987	0.959	0.980	0.983	0.916	0.992	0.985	0.970	0.980	0.989	0.976	0.982	0.976	0.967	0.974
	AUC	0.990	0.999	0.995	0.997	0.999	0.990	0.999	0.999	0.996	0.999	0.999	0.995	0.999	0.999	0.998	0.997
Ablation 1	Acc	0.961	0.985	0.956	0.981	0.987	0.902	0.992	0.987	0.972	0.984	0.985	0.967	0.982	0.978	0.967	0.972

Appendix

Table 10: Ablation of VJEPA-2 test set

Model	Metric	Veo3	Sora2	HY	CVX	EA	LTX	Pika1	Wan2	Kling2	Sora	Avg
Base	Acc	0.984	0.982	0.989	0.986	0.991	0.987	0.989	0.985	0.988	0.985	0.971
	Prec	0.985	0.980	0.981	0.981	0.983	0.978	0.989	0.978	0.987	0.985	0.976
	Recall	0.982	0.984	0.997	0.992	0.999	0.997	0.990	0.993	0.990	0.986	0.968
	F1	0.984	0.982	0.989	0.987	0.991	0.988	0.989	0.986	0.989	0.986	0.972
	AUC	0.998	0.998	1.000	0.999	1.000	1.000	0.999	0.999	0.999	0.997	0.995
Ablation 1	Acc	0.988	0.976	0.991	0.991	0.993	0.990	0.988	0.978	0.990	0.981	0.970

Appendix

Table 11: Ablation of CLIP val set

Model	Metric	Unk	RM2	Kling1	Hailuo	SD	Mochi	JM	Gen3	Luma	Vidu	PRM	SKR	PV	Pika2	Gen4	Avg
Base	Acc	0.961	0.987	0.959	0.980	0.983	0.920	0.992	0.985	0.970	0.979	0.988	0.976	0.982	0.975	0.967	0.974
	Prec	0.980	0.985	0.984	0.984	0.986	0.986	0.986	0.980	0.990	0.968	0.988	0.980	0.972	0.984	0.993	0.983
	Recall	0.943	0.989	0.936	0.977	0.981	0.855	0.998	0.990	0.952	0.992	0.990	0.972	0.993	0.968	0.942	0.965
	F1	0.961	0.987	0.959	0.980	0.983	0.916	0.992	0.985	0.970	0.980	0.989	0.976	0.982	0.976	0.967	0.974
	AUC	0.990	0.999	0.995	0.997	0.999	0.990	0.999	0.999	0.996	0.999	0.999	0.995	0.999	0.999	0.998	0.997
Ablation 1	Acc	0.952	0.994	0.965	0.981	0.985	0.783	0.993	0.989	0.982	0.995	0.992	0.716	0.924	0.871	0.721	0.923

Appendix

Table 12: Ablation of CLIP val set

Model	Metric	Veo3	Sora2	HY	CVX	EA	LTX	Pika1	Wan2	Kling2	Sora	Avg
Base	Acc	0.963	0.960	0.978	0.972	0.989	0.983	0.978	0.957	0.988	0.945	0.971
	Prec	0.979	0.973	0.977	0.975	0.980	0.973	0.977	0.975	0.981	0.972	0.976
	Recall	0.947	0.948	0.980	0.971	0.999	0.994	0.980	0.941	0.996	0.919	0.968
	F1	0.963	0.960	0.978	0.973	0.990	0.983	0.978	0.958	0.989	0.945	0.972
	AUC	0.994	0.991	0.997	0.994	1.000	0.999	0.997	0.987	1.000	0.986	0.995
Ablation 1	Acc	0.960	0.959	0.978	0.972	0.989	0.980	0.974	0.956	0.986	0.948	0.970

Appendix

Table 13: Ablation of DINO-v3 val set

Model	Metric	Unk	RM2	Kling1	Hailuo	SD	Mochi	JM	Gen3	Luma	Vidu	PRM	SKR	PV	Pika2	Gen4	Avg
Base	Acc	0.931	0.990	0.848	0.976	0.973	0.770	0.974	0.962	0.969	0.980	0.969	0.683	0.915	0.794	0.638	0.891
	Prec	0.973	0.982	0.977	0.984	0.971	0.957	0.972	0.977	0.978	0.984	0.960	0.942	0.971	0.937	0.979	0.970
	Recall	0.889	0.998	0.719	0.968	0.976	0.576	0.976	0.948	0.962	0.978	0.980	0.404	0.859	0.640	0.299	0.811
	F1	0.929	0.990	0.828	0.976	0.974	0.719	0.974	0.962	0.970	0.981	0.970	0.565	0.912	0.760	0.458	0.865
	AUC	0.981	1.000	0.960	0.997	0.996	0.906	0.996	0.994	0.996	0.997	0.996	0.909	0.979	0.929	0.912	0.970
Ablation 1	Acc	0.933	0.988	0.849	0.971	0.973	0.766	0.974	0.958	0.971	0.981	0.973	0.693	0.923	0.780	0.665	0.893

Appendix

Table 14: Ablation of DINO-v3 test set

Model	Metric	Veo3	Sora2	HY	CVX	EA	LTX	Pika1	Wan2	Kling2	Sora	Avg
Base	Acc	0.946	0.931	0.967	0.960	0.976	0.964	0.944	0.947	0.971	0.935	0.954
	Prec	0.956	0.955	0.959	0.959	0.961	0.948	0.949	0.958	0.967	0.952	0.956
	Recall	0.937	0.907	0.978	0.961	0.994	0.983	0.941	0.938	0.976	0.919	0.953
	F1	0.946	0.931	0.968	0.960	0.977	0.965	0.945	0.948	0.972	0.935	0.955
	AUC	0.989	0.983	0.995	0.992	0.999	0.994	0.989	0.983	0.994	0.983	0.990
Ablation 1	Acc	0.945	0.932	0.971	0.963	0.978	0.967	0.942	0.948	0.973	0.936	0.955

Appendix

Table 15: Ablation of DINO-v2 val set

Model	Metric	Unk	RM2	Kling1	Hailuo	SD	Mochi	JM	Gen3	Luma	Vidu	PRM	SKR	PV	Pika2	Gen4	Avg
Base	Acc	0.920	0.973	0.828	0.963	0.958	0.742	0.963	0.943	0.949	0.974	0.944	0.667	0.895	0.775	0.621	0.874
	Prec	0.954	0.962	0.955	0.970	0.966	0.921	0.966	0.955	0.946	0.987	0.956	0.921	0.966	0.941	0.870	0.949
	Recall	0.886	0.987	0.695	0.958	0.952	0.541	0.962	0.932	0.954	0.961	0.932	0.381	0.823	0.597	0.305	0.791
	F1	0.919	0.974	0.805	0.964	0.959	0.682	0.964	0.943	0.950	0.974	0.944	0.539	0.889	0.730	0.452	0.846
	AUC	0.975	0.997	0.935	0.992	0.990	0.882	0.992	0.984	0.991	0.997	0.984	0.886	0.972	0.923	0.862	0.957
Ablation 1	Acc	0.923	0.975	0.845	0.963	0.948	0.735	0.964	0.941	0.951	0.963	0.932	0.667	0.893	0.791	0.638	0.875

Appendix

Table 16: Ablation of DINO-v2 test set

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