Verus-Insight: Towards Explainable Artifact Detection in AI-Generated Videos

Zhuoran Zhang; Yang Shi; Qixun Wang; Tengfei Liu; Yuqi Tang; Xuanyu Zhu; Yizhuo Jia; Jingyun Hua

cs.CVOct 22, 2026

Verus-Insight: Towards Explainable Artifact Detection in AI-Generated Videos

Authors: Zhuoran Zhang, Yang Shi, Qixun Wang, Tengfei Liu, Yuqi Tang, Xuanyu Zhu, Yizhuo Jia, Jingyun Hua,

Organizations: Peking University · Kling Team · HKUST (GZ) · Fudan University ·

Abstract

Recent advances in video generation have made synthetic videos increasingly realistic, placing greater demands on reliable AI-generated video detection. Existing artifact-based detectors typically identify generation artifacts as evidence for source prediction, yet they may either fail to recognize subtle artifacts or reach the correct source decision without faithfully identifying the evidence underlying it. In this work, we systematically investigate how to design and train effective artifact detectors and formulate artifact detection as a structured process comprising three complementary capabilities: Inspection Planning, which determines what should be examined; Evidence Verification, which verifies whether the inspected content exhibits genuine generation artifacts; and Evidence Aggregation, which integrates the verified evidence into the final source decision. Building on this formulation, we introduce Verus-Insight, trained with human-anchored full-process supervision and progressive capability training to strengthen both fine-grained artifact detection and video-level detection. We further introduce Verus-Bench, a benchmark with fine-grained human annotations of perceptible artifacts for evaluating artifact detection. Extensive experiments across diverse benchmarks demonstrate that Verus-Insight achieves strong performance in both fine-grained artifact analysis and AI-generated video detection, with substantial gains in recovering human-perceived artifacts. Further analyses reveal that the three capabilities are complementary yet distinct, and that Verus-Insight more reliably converts relevant inspection targets into supported artifact evidence without increasing spurious artifact claims.

Figures & tables

Figure 1: Towards explainable and reliable AI-generated video detection. Conventional artifact detectors may make incorrect source predictions or reach the correct conclusion with inaccurate artifact evidence. Verus-Insight instead performs Inspection Planning , Evidence Verification , and Evidence Aggregation , grounding source decisions in explicitly verified, human-perceivable artifact evidence for more explainable and reliable detection.

Figure 2: Distinct behaviors in Artifact Detection. Skyra misses the primary temporal artifact without inspecting the relevant change; VideoVeritas observes the change but misinterprets it as normal styling; BusterX++ reaches the correct source prediction without recovering the salient artifact evidence; Verus-Insight inspects the relevant regions and correctly recovers the salient artifacts.

(a) Inspection Planning
Warm-up supervision	Explicit interface	Plan F1 $\uparrow$	Finding F1 $\uparrow$	BA $\uparrow$
Rubric warm-up	Explicit rubric
No	No	19.76	18.37	78.30
No	Yes	22.63	17.10	77.43
Yes	No	18.75	15.94	84.55
Yes	Yes	23.61	18.68	86.11

Table 1: Development studies for Inspection Planning and Evidence Verification.

Figure 3: Data construction and training pipeline of Verus-Insight. Human artifact annotations and verified-normal checks are organized into criterion-linked verification trajectories for full-process supervision. Training proceeds through tracking and rubric-planning warm-up, full-process SFT, and reinforcement learning.

Proprietary Models
Model	Artifact-Bench			RewardData	ViF-Bench	Verus-Bench
Model	T1	T2	T3	RewardData	ViF-Bench	Verus-Bench
Gemini-3.1-Pro	69.60	57.20	10.29	51.60	52.98	51.04
Gemini-3.6-Flash	66.80	62.00	12.00	90.52	72.22	88.02
GPT-5.5	53.60	52.80	11.43	93.81	62.66	89.06
Open-Source General-Purpose Models

Table 2: Video-level detection results . Artifact-Bench reports BA, accuracy, and exact-match accuracy for T1–T3, respectively; all other benchmarks report BA. Among open-source models, best results are bolded and highlighted, and second best results are underlined.

Proprietary Models
Model	Verus-Bench						RewardData
	Plan			Finding			Plan			Finding
	P	R	F1	P	R	F1	P	R	F1	P	R	F1
Gemini-3.1-Pro	26.35	27.41	26.87	13.91	26.17	18.16	22.83	46.15	30.55	6.29	42.86	10.97
Gemini-3.6-Flash	31.15	37.07	33.85	25.67	29.91	27.63	22.98	59.34	33.13	16.64	54.95	25.54
GPT-5.5	23.93	45.17	31.28	18.47	32.40	23.53	14.69	65.93	24.02	10.84	54.40	18.08

Table 3: Fine-grained artifact planning and finding results . Plan is evaluated on artifact-positive videos, while Finding is evaluated on the full set.

	Verus-Bench			RewardData
Variant	BA	Plan F1	Finding F1	BA	Plan F1	Finding F1
Qwen3-VL-8B Base	66.67	17.08	7.56	48.81	18.67	3.38
Verus-Insight	89.06	24.70	15.97	94.79	31.33	35.42
w/o Stage-II rubric	90.63	21.54	13.76	96.07	26.57	29.35
w/o Perception Warm-up	88.02	22.49	14.40	94.03	28.47	33.16
w/o rubric Warm-up	87.50	22.03	14.29	93.70	27.87	32.24

Table 4: Ablation study of Verus-Insight.

Figure 4: Relationships between stage-wise artifact-detection capabilities. Panels (a,c) compare Plan and Finding F1, while (b,d) compare Finding F1 with video-level BA. Each point denotes a model; dashed diagonals in (a,c) indicate equal Plan and Finding F1.

Figure 5: Diagnostic analysis of artifact recovery. (a) Plan-to-evidence conversion,

P(F=1\mid P=1)

. (b) Strict Artifact FPR on real RewardData videos. (c) Plan and Finding F1 on ViF-CoT-4K subset; Skyra is excluded due to training-data overlap.

Figure 6: Artifact-recovery gains across different conditions. (a,b) Any-Hit rate and artifact coverage across artifact counts; (c,d) artifact recall across temporal and spatial extents. All results use Verus-Bench;

n

denotes videos in (a,b) and reference artifacts in (c,d).

Appendix figures & tables9 assets

Supplementary material from the paper’s appendix.

Appendix

Source	Instances	Real / AI-generated	Rubric criteria	Artifact checks	Normal checks	Other checks
Verus-Annotation	6,411	2,875 / 3,536	32,220	10,439	21,577	204
RewardData	2,000	1,000 / 1,000	4,089	1,306	2,782	1
Total	8,411	3,875 / 4,536	36,309	11,745	24,359	205

Appendix

Table 5: Training data composition and supervision counts after enrichment. Each training instance is counted once.

Figure 7: Distributions of Rubric criteria, artifact checks, and normal checks per enriched training instance (

n=8,411

Figure 8: Verus-Bench distributions over 192 videos: (a) reference artifacts per video; (b) sampled input frames.

Figure 9: Artifact-category distribution of Verus-Bench, covering 326 reference findings across 18 observed categories, ordered by frequency.

Source	Source pool	Videos	Real	AI-generated	Artifact findings
VERUS-Annotation	Dev split	192	96	96	323
RewardData	Official val split	192	96	96	126
ViF-CoT	Source training pool	192	96	96	96
Total		576	288	288	545

Appendix

Table 6: Analysis-Dev composition.

Setting	Stage I	Stage II	Stage III
Parameter updates	Full parameters	Full parameters	LoRA ( $r=\alpha=32$ )
Learning rate	$10^{-5}$	$10^{-5}$	$10^{-6}$
Global batch size	96	96	64 prompts
Training budget	72 updates	176 updates	At most 128 rollout rounds
Learning-rate schedule	5% warm-up, then cosine	5% warm-up, then cosine	Short warm-up, then constant

Appendix

Table 7: Training hyperparameters. SFT batch sizes count sequences, while the RL batch size counts video prompts.

Proprietary Models
Model	Verus-Bench			RewardData
Model	Any-Hit	Macro	Micro	Any-Hit	Macro	Micro
Gemini-3.1-Pro	65.26	26.51	26.17	49.67	46.03	42.86
Gemini-3.6-Flash	70.53	30.98	29.91	62.91	57.51	54.95
GPT-5.5	65.26	32.88	32.40	58.94	55.74	54.40
Open-Source General-Purpose Models

Appendix

Table 8: Additional aggregate artifact-coverage results (%). The common positive cohorts contain 95 Verus-Bench videos and 151 RewardData videos. Macro and Micro denote macro- and micro-averaged coverage. Proprietary models are shown in gray for reference. The best and second-best results among open-source models are highlighted in green and underlined, respectively.

Figure 10: Artifact-recovery analysis on RewardData. (a,b) Any-Hit rate and macro coverage for videos with one or multiple reference artifacts. (c,d) Artifact recall across temporal and spatial extents.

n

denotes the number of videos in (a,b) and reference artifacts in (c,d). Grouping details are provided in Appendix A.4 .

Panel	Extent	$q_{1}$	$q_{2}$	Low / medium / high
Verus-Bench	Temporal	0.100000	0.175000	133 / 85 / 103
	Spatial	0.035771	0.160534	107 / 107 / 107
RewardData	Temporal	0.525000	0.983333	60 / 60 / 58
	Spatial	0.156621	0.386096	61 / 60 / 61

Appendix

Table 9: Extent-bin thresholds (rounded for display) and reference counts. The hits are semantic recovery events, not localization successes.

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