ManiVid: Unified and Explainable Forensic Analysis of Manipulated Videos

Hengrui Kang; Zhonghao Yan; Yuxuan Yang; Ruoyan Jing; Yuncheng Guo; Hao Chen; Kongming Liang; Zhanyu Ma

cs.CVSep 25, 2026

ManiVid: Unified and Explainable Forensic Analysis of Manipulated Videos

Authors: Hengrui Kang, Zhonghao Yan, Yuxuan Yang, Ruoyan Jing, Yuncheng Guo, Hao Chen, Kongming Liang, Zhanyu Ma,

Organizations: Shanghai Jiao Tong University · Shanghai AI Laboratory · Beijing University of Posts and Telecommunications · Fudan University ·

Abstract

Rapid advances in AI-generated video (AIGV) have increased the risks posed by deceptive video manipulation. Unlike fully synthetic videos, manipulated videos retain most source content and alter only localized regions, making forensic analysis particularly challenging. Existing video forgery research faces two limitations in both data and methodology: (1) High-quality datasets and benchmarks tailored for manipulated videos remain scarce. (2) Multimodal large language models (MLLMs) extend forgery analysis beyond binary classification but struggle to use low-level forensic cues and provide precise pixel-level grounding. Specifically, we introduce ManiVid, a unified forensic analysis task covering forgery detection, artifact grounding, and anomaly explanation for manipulated videos. We construct ManiVid-38K, the first dataset to combine paired, open-vocabulary localized manipulations of general videos with authenticity labels, forgery masks, and anomaly explanations. It comprises about 19K manually verified real-fake video pairs, mostly at 1080P resolution, generated under 2 paradigms with 15 powerful generation models. We sample 1K pairs for ManiVidBench, balanced across six manipulation types and generation models for fair evaluation. We further propose ManiVidLens, a unified framework for explainable video forgery analysis. Its Forensic Evidence Router supplies shared low-level forensic evidence for multimodal reasoning and video segmentation. Its Prompt Distill Module converts grounding states into semantic and geometric prompts and distills spatial priors for mask decoding and full-video propagation. ManiVidLens achieves relative gains over the strongest comparison methods in artifact grounding (+21.1% mIoU; +21.3% J&F) and anomaly explanation (+131.3% ROUGE-L; +9.9% CSS). Its forgery detection remains comparable to dedicated classifiers (0.914 Acc; 0.913 F1).

Figures & tables

Figure 1: Overview of ManiVid. ManiVid unifies forgery detection, artifact grounding, and anomaly explanation. The ManiVid-38K dataset provides paired, open-vocabulary manipulations across six types with authenticity labels, pixel-level masks, and anomaly explanations. ManiVidBench provides 2K videos for evaluation, and ManiVidLens offers an end-to-end solution.

Figure 2: Dataset Curation. ManiVid collects high-quality real videos from three public datasets and uses the MLLM assistant to generate open-vocabulary , reasonable , and feasible manipulation instructions. Videos are then manipulated by diverse models, followed by the auto reflection and human verification. ( $\cdot$ ) denotes the number of generation models used.

Dataset	Domain	Video Generation				Comprehensive Annotation					Scale
Dataset	Domain	Task	Model	Paradigm	V.P.	O.V.I	Label	Mask	Explanation	H.V.	Scale
Forgery Type: Fully Synthetic (Directly applying T2V and I2V models)
VidProM ( Wang and Yang, 2024 )	General	-	4	T2V	✗	✔	✔	✗	✗	✗	6.69M
GenVideo ( Chen et al., 2026 )	General	-	20	T2V, I2V	✗	✗	✔	✗	✗	✗	2.31M
AEGIS ( Li et al., 2025 )	General	-	10	T2V, I2V	✗	✗	✔	✗	✔	✔	10470
GenVideoBench ( Ni et al., 2026 )	General	-	11	T2V, I2V	✔	✔	✔	✗	✗	✗	6.78M

Table 1: Comparison with Existing Video Forgery Datasets. V.P. denotes the availability of semantic-aligned real-fake video pairs; O.V.I. denotes the release of open-vocabulary instructions; H.V. denotes human verification. Datasets that cannot guarantee source videos are real are excluded .

Figure 3: Model Architecture. (a) ManiVidLens unifies forgery detection, artifact grounding, and anomaly explanation. (b) The Forensic Evidence Router (FER) builds a compact forensic evidence bank and routes it to the MLLM and SAM2. (c) The Prompt Distill Module (PDM) converts MLLM grounding-token states into SAM2 prompts. Dashed arrows mark training-only paths.

Figure 4: Qualitative Comparison of ManiVidLens with Different Models. We visualize two representative manipulation cases: (Left) adding a knife and (Right) changing the refrigerator material to steel. Across both cases, ManiVidLens demonstrates consistently better forensic analysis than LEGION (Image-based), Sa2VA (Video-based), and GPT-5.6 Sol (General MLLM).

Method	Focus	ManiVidBench												Avg.
		Addition		Removal		Replacement		Color		Material		Portrait
		mIoU	J&F	mIoU	J&F	mIoU	J&F	mIoU	J&F	mIoU	J&F	mIoU	J&F	mIoU	J&F
Image Expert Models (3)
LEGION ( Kang et al., 2025 )	Synth.	0.343	0.352	0.091	0.102	0.385	0.392	0.323	0.346	0.360	0.370	0.192	0.213	0.312	0.324
FakeShield ( Xu et al., 2025 )	Mani.	0.365	0.374	0.068	0.073	0.332	0.333	0.429	0.430	0.373	0.371	0.161	0.153	0.318	0.320
SIDA ( Huang et al., 2025a )	Both	0.013	0.113	0.004	0.020	0.010	0.019	0.020	0.042	0.009	0.025	0.002	0.004	0.011	0.041

Table 2: Artifact Grounding Performance. Synth. and Mani. denote fully synthetic and manipulated content, respectively. marks frame-wise approximations on 30 uniformly sampled frames;

\bm{\square}

marks native box outputs evaluated with shared SAM2 propagation over every video frame.

Table 4: Forgery Detection Performance. Synth. and Mani. denote fully synthetic and manipulated content. Acc and F1 denote video-level accuracy and F1; Avg. covers all 2,000 videos. Image expert models use majority voting over 30 uniformly sampled frames at a

0.5

threshold.

Appendix figures & tables39 assets

Supplementary material from the paper’s appendix.

Appendix

Dimension	Full Synthesis	Manipulation
Source Video Reference	None or partial	Complete
Generation Paradigm	Direct T2V / I2V	V2V / Conditional I2V
Modification Scope	Entire video	Localized regions
Real–Fake Video Pair	Unavailable	Naturally paired
Semantic Alignment	Low	High
Visual Alignment	Low	High

Appendix

Table 7: Scope Comparison between Full Synthesis and Manipulation.

Source Dataset	OpenVid-1M	InternVid	OpenVE-3M	Total
Sampled Num	222391	5882	15000	243273
+ Deduplication	139697	3974	9292	152963
+ Quality Filter	35643	2959	2506	41108

Appendix

Table 8: Data Statistics During Collection. The number of source videos retained at different stages of the collection process.

Content Category	Definition	Examples
Human	The video is centered on one or more humans. The person(s) usually occupy a large portion of the frame or are the clear narrative focus. The background is typically simple and does not form a complete environment. Notes: (1) If humans appear but are small and the environment is the main focus, consider Indoor or Outdoor instead. (2) If the video mainly conveys a time-continuous activity or process where “what is happening” is more important than “who the person is”, consider Event .	Reporter, Police Officer, Businessman, Farmer, …
Animal	The video is centered on one or more animals, which occupy a large portion of the frame or are the narrative focus. Notes: (1) If animals are small or only decorative and the environment dominates, use Indoor or Outdoor . (2) If the video mainly presents a continuous activity performed by the animals, consider Event .	Cat, Dog, Bird, Horse, Cow, Sheep, …
Object	The video focuses on one or a few inanimate objects. The background is simple and does not form a complete environment. Notes: (1) Hands or partial human bodies may appear, but the object itself is the core subject. (2) If the video emphasizes a multi-step, time-continuous process, consider Event .	Knife, Fork, Food, Tool, Cup, Toy, Laptop, …
Indoor	The video primarily presents a complete and visually rich indoor environment (e.g., rooms, offices, shops). No single subject dominates, or subjects are small and secondary. Notes: (1) Use this when the spatial layout, interior structure, and overall atmosphere are the main information.	Room, Office, Shop, Gym, Hospital, …
Outdoor	The video primarily presents a complete and visually rich outdoor environment or scenery (e.g., natural landscapes, city streets, building exteriors, aerial views). No single subject dominates, or subjects are small and secondary. Notes: (1) Even if people or animals appear, choose Outdoor as long as they are minor and the scenery is the main focus.	Natural Landscape, City Street, Aerial View, …
Event	The video is centered on a time-continuous activity or event. The key information lies in what is happening over time rather than the appearance of a specific person, animal, object, or environment. Notes: (1) It includes any multi-step actions with clear temporal progression. (2) The main subject may be human, animal, or object, but the activity itself is the narrative core.	Cooking Process, Ceremony, Sports Game, …

Appendix

Table 9: Content Category Definitions and Examples.

Panel of Table 10 — Table 10: Content Categorization Prompt.

Panel of Table 11 — Table 11: Video Recaption Prompt.

Manipulation Task	Definition	Example
Addition	Integrate a new, semantically relevant object into the scene.	Add a red backpack onto the empty bench in <video> .
Removal	Erase a specific object with clear boundaries and inpaint the background naturally.	Remove the blue suitcase from <video> , seamlessly inpainting the revealed pavement.
Replacement	Swap an object with a fine-grained variant or a logically related entity.	Replace the white sedan in <video> with a red SUV, preserving the original motion trajectory.
Color Alteration	Change the color of a specific object while preserving its original texture and shape.	Change the color of the woman’s jacket in <video> to blue, maintaining its original fabric texture.
Material Modification	Alter the physical material properties.	Change the material of the rotating vase in <video> into polished metal, maintaining its original motion.
Portrait Editing	Modify facial features, expressions, or skin tones. ( Note : Only applicable to close-up or medium shots of people).	Modify the facial features of the woman at the center of <video> to exhibit a smiling expression, keeping others consistent.

Appendix

Table 12: Manipulation Task Definitions and Examples.

Paradigm	V2V Model	(Stage 1) Image Editing Model	(Stage 2) Canny-guided I2V Model
Direct V2V (53.80%)	Kling-O1 (5.80%) Kling-V3-Omni (2.65%) Runway Aleph (8.48%) Wan 2.2-Video-Edit (10.43%) VideoCoF (21.36%) VACE (4.39%) VideoPainter (0.69%)	–	–
Nano Banana 2 (13.92%) Seedream 5.0 (14.16%) GPT-Image-1.5 (0.39%) FLUX.2-dev-32B (5.75%) FLUX.2-klein-9B (3.34%) Qwen-Image (8.64%) Indirect I2V (46.20%)	Nano Banana 2 (13.92%) Seedream 5.0 (14.16%) GPT-Image-1.5 (0.39%) FLUX.2-dev-32B (5.75%) FLUX.2-klein-9B (3.34%) Qwen-Image (8.64%) –	Nano Banana 2 (13.92%)	Kling-V3-Omni (1.77%) Wan 2.2 (5.54%) LTX-2 (6.60%)
		Seedream 5.0 (14.16%)	Kling-V3-Omni (1.81%) Wan 2.2 (7.72%) LTX-2 (4.63%)
		GPT-Image-1.5 (0.39%)	Kling-V3-Omni (0.00%) Wan 2.2 (0.39%) LTX-2 (0.00%)
		FLUX.2-dev-32B (5.75%)	Kling-V3-Omni (1.14%) Wan 2.2 (1.27%) LTX-2 (3.34%)
		FLUX.2-klein-9B (3.34%)	Kling-V3-Omni (0.00%) Wan 2.2 (1.41%) LTX-2 (1.93%)

Appendix

Table 14: Detailed Distribution of Manipulation Paradigms and Models in ManiVid-38K.

Panel of Figure 5 — Figure 5: Manipulation Examples of Seven Direct V2V Models. The first four rows are closed source models, while the last three rows are open source models. Zoom in for a better view.

Panel of Figure 6 — Figure 6: Manipulation Examples of three indirect I2V Models. The first row is Kling-V3-Omni (closed source), while the last two rows are open source models. Zoom in for a better view.

Panel of Table 15 — Table 15: Automatic Reflection Prompt.

Anomaly Perspective	Definition
Boundary Coherence	Imperfect transitions between the manipulated region and the original footage, including overly smoothed or unnaturally sharp boundary edges, as well as texture discrepancies in surface noise, resolution, or material quality relative to the surrounding unedited areas.
Perspective and Proportion	Geometric spatial errors where the manipulated content violates the camera perspective, exhibits focal-length-related distortions, or shows unnatural scaling and structural proportions relative to its immediate environment.
Contextual Inconsistency	Semantic or logical anomalies where the manipulated content defies the established scene context, including physically impossible object interactions, gravity-defying kinematics, or elements that fundamentally contradict surrounding real-world physics.
Color and Lighting	Mismatches in illumination, shadow trajectories, or color-space distribution between the manipulated content and the global physical environment.
Texture Discontinuity	Sudden degradation in surface quality, excessive smoothing, or unnatural loss of high-frequency details within the manipulated region.
Temporal Flicker	Unstable pixel generation or erratic high-frequency variations of static visual features across consecutive frames, typically manifested as localized shimmering or micro-jitter in textures that should remain structurally rigid.

Appendix

Table 16: Definitions of Anomaly Explanation Perspectives.

Figure 7: Additional statistical analyses of ManiVid-38K.

Panel of Figure 8 — Figure 8: Examples of Addition and Removal in ManiVid-38K. Zoom in for a better view.

Panel of Figure 9 — Figure 9: Examples of Replacement and Color Alteration in ManiVid-38K. Zoom in for a better view.

Panel of Figure 10 — Figure 10: Examples of Material Modification and Portrait Editing in ManiVid-38K. Zoom in for a better view.

Figure 11: Statistical Analysis of Our ManiVidBench.

Task protocol	Instruction objective and supervised assistant response format
Classification	Determine the manipulation status and one applicable manipulation type. The response contains Manipulation status: followed by Manipulation type: .
Explanation	Describe the relevant visual, physical, and temporal anomalies. The response contains exactly one <think>...</think> reasoning block.
Segmentation	Locate the manipulated region and describe its target briefly. A positive response contains the target fields and exactly one $[\mathrm{SEG}]$ marker.

Appendix

Table 18: Task-specific generation protocols used by ManiVidLens. Each row summarizes one instruction objective and its exact assistant response structure.

Panel of Table 19 — Table 19: Task Prompts Used in the ManiVidLens Framework. Separate prompts are the default; the All-in-One prompt is used only for the 3-task ablation.

Figure 12: Training and Inference Pipelines of ManiVidLens. (a) Stage 1 learns task-specific responses and authenticity routing. Stage 2 adds box-prompt distillation and sampled-frame mask supervision, with PDM reading teacher-forced token states. (b) Inference constructs SAM2 prompts from generated token states and propagates masks across the full video using only video and task inputs. GT denotes ground-truth responses, boxes, and masks used for supervision.

Training aspect	Stage 1 implementation details	Stage 2 implementation details
Stage length	One complete training epoch	One complete training epoch
Task setup	Three separate task instances with $1/3$ shared-loss scaling	Three separate task instances with $1/3$ shared-loss scaling
Batch ratio	Balanced original-to-manipulated ratio of $1{:}1$	Manipulation-focused original-to-manipulated ratio of $1{:}3$
Shared weights	Language and classification weights are $\lambda_{\mathrm{lm}}=1.0$ and $\lambda_{\mathrm{cls}}=1.0$	Language and classification weights are $\lambda_{\mathrm{lm}}=0.1$ and $\lambda_{\mathrm{cls}}=0.5$
Grounding weights	Every grounding-loss weight remains zero	$\lambda_{m}=2.0$ , $\lambda_{d}=1.0$ , and $\lambda_{c}=1.0$
Class weight	The classifier uses unit positive-class weight	The classifier uses one-third positive-class weight

Appendix

Table 20: Two-stage training implementation for the current full profile. The table reports the launcher defaults and stage-specific optimization settings.

Task	Metric	Direct V2V (52.3%)								Indirect I2V (47.7%)				Overall
Task	Metric	Kling-O1	Kling-V3 Omni	Runway Aleph	Wan 2.2 Video-Edit	VideoCoF	VACE	VideoPainter	Avg.	Kling-V3 Omni	Wan 2.2	LTX-2	Avg.	Overall
Grounding	mIoU	0.435	0.419	0.452	0.553	0.317	0.396	0.465	0.413	0.292	0.383	0.440	0.398	0.407
Grounding	J&F	0.450	0.436	0.466	0.574	0.333	0.414	0.469	0.429	0.303	0.402	0.452	0.413	0.422
Explanation	R-L	0.533	0.557	0.605	0.621	0.553	0.596	0.607	0.567	0.615	0.608	0.600	0.605	0.583
Explanation	CSS	0.686	0.740	0.828	0.855	0.722	0.796	0.789	0.750	0.835	0.826	0.811	0.821	0.780
Detection	Acc	0.751	0.941	0.972	0.976	0.850	0.974	1.000	0.868	0.980	0.967	0.982	0.975	0.914

Appendix

Table 21: ManiVidLens Performance by Generation Model. (

\cdot

) indicate each generation paradigm’s share of ManiVidBench. Indirect I2V groups follow the final video generator, pooling over first-frame editors. Avg. aggregates each paradigm, while Overall matches the main tables under the evaluation protocol in Appendix E.1.3 .

Figure 13: Confusion Matrix for Seven-Class Manipulation Attribution.

Setting	ManiVidBench												Avg.
	Addition		Removal		Replacement		Color		Material		Portrait		Avg.
	mIoU	J&F	mIoU	J&F	mIoU	J&F	mIoU	J&F	mIoU	J&F	mIoU	J&F	mIoU	J&F
Ablation 1: Number of Sampled Frames
3 Frames	0.411	0.432	0.118	0.118	0.366	0.382	0.395	0.417	0.450	0.456	0.278	0.286	0.354	0.368
5 Frames	0.444	0.467	0.135	0.137	0.421	0.439	0.487	0.507	0.526	0.532	0.290	0.306	0.407	0.422
7 Frames	0.508	0.533	0.114	0.120	0.440	0.460	0.452	0.474	0.483	0.494	0.353	0.370	0.413	0.431

Appendix

Table 22: Category-wise Ablation for Artifact Grounding. Column groups follow the six manipulation categories, with benchmark-level scores in Avg. Metrics are mIoU and J&F (

\uparrow

Setting	ManiVidBench												Avg.
	Addition		Removal		Replacement		Color		Material		Portrait		Avg.
	R-L	CSS	R-L	CSS	R-L	CSS	R-L	CSS	R-L	CSS	R-L	CSS	R-L	CSS
Ablation 1: Number of Sampled Frames
3 Frames	0.587	0.791	0.574	0.761	0.571	0.756	0.575	0.756	0.578	0.781	0.544	0.695	0.575	0.763
5 Frames	0.603	0.813	0.576	0.773	0.581	0.777	0.582	0.772	0.587	0.795	0.545	0.699	0.583	0.780
7 Frames	0.613	0.831	0.591	0.796	0.593	0.797	0.592	0.801	0.603	0.817	0.555	0.721	0.596	0.803

Appendix

Table 23: Category-wise Ablation for Anomaly Explanation. Column groups follow the six manipulation categories in the main results, with benchmark-level scores in Avg. Metrics are ROUGE-L (R-L) and Cosine Similarity Score (CSS) (

\uparrow

Setting	ManiVidBench												Avg.
	Addition		Removal		Replacement		Color		Material		Portrait		Avg.
	Acc	F1	Acc	F1	Acc	F1	Acc	F1	Acc	F1	Acc	F1	Acc	F1
Ablation 1: Number of Sampled Frames
3 Frames	0.953	0.953	0.920	0.920	0.885	0.885	0.906	0.906	0.912	0.912	0.805	0.803	0.906	0.905
5 Frames	0.966	0.966	0.943	0.943	0.868	0.868	0.917	0.917	0.939	0.939	0.839	0.839	0.914	0.913
7 Frames	0.971	0.971	0.958	0.958	0.899	0.899	0.930	0.930	0.931	0.931	0.822	0.821	0.926	0.926

Appendix

Table 24: Category-wise Ablation for Forgery Detection. Column groups follow the six manipulation categories in the main results, with benchmark-level scores in Avg. Metrics are video-level Accuracy (Acc) and F1 (

\uparrow

) for original and manipulated videos.

Prompt Design	ManiVidBench												Avg.
Prompt Design	Addition		Removal		Replacement		Color		Material		Portrait		Avg.
Task Mode: Classification
Metrics	Acc	F1	Acc	F1	Acc	F1	Acc	F1	Acc	F1	Acc	F1	Acc	F1
All-in-one	0.838	0.833	0.779	0.768	0.777	0.766	0.758	0.744	0.790	0.782	0.627	0.567	0.778	0.767
Separate (Ours)	0.966	0.966	0.943	0.943	0.868	0.868	0.917	0.917	0.939	0.939	0.839	0.839	0.914	0.913
Task Mode: Explanation

Appendix

Table 25: Category-wise Ablation on Prompt Design. All-in-one and separate prompts are compared across six manipulation categories and three tasks. Avg. reports benchmark-level scores. Higher metric values (

\uparrow

) indicate better performance on all three tasks.

Setting		Grounding		Explanation		Detection
Setting		mIoU	J&F	R-L	CSS	Acc	F1
ManiVidLens		0.407	0.422	0.583	0.780	0.914	0.913
H.264 Compression	CRF 23	0.384 (-5.5%)	0.400 (-5.2%)	0.562 (-3.7%)	0.735 (-5.8%)	0.882 (-3.4%)	0.882 (-3.5%)
H.264 Compression	CRF 40	0.246 (-39.5%)	0.255 (-39.5%)	0.514 (-11.8%)	0.638 (-18.2%)	0.591 (-35.3%)	0.535 (-41.5%)
Downsampling	$\times$ 0.75	0.399 (-2.0%)	0.416 (-1.4%)	0.577 (-1.2%)	0.775 (-0.6%)	0.895 (-2.0%)	0.895 (-2.0%)
Downsampling	$\times$ 0.50	0.400 (-1.7%)	0.415 (-1.7%)	0.567 (-2.7%)	0.764 (-2.0%)	0.859 (-6.0%)	0.859 (-6.0%)

Appendix

Table 26: Robustness Evaluation under Different Video Degradations. Grounding, explanation, and detection scores (

\uparrow

) are evaluated under four types of video degradation. Parenthesized values show signed percentage changes relative to the undegraded ManiVidLens baseline, with negative values indicating performance degradation for the corresponding metric.

Figure 14: A ManiVidLens Case on an Addition Sample.

Figure 15: A ManiVidLens Case on an Addition Sample.

Figure 16: A ManiVidLens Case on a Removal Sample.

Figure 17: A ManiVidLens Case on a Removal Sample.

Figure 18: A ManiVidLens Case on a Replacement Sample.

Figure 19: A ManiVidLens Case on a Replacement Sample.

Figure 20: A ManiVidLens Case on a Color Alteration Sample.

Figure 21: A ManiVidLens Case on a Color Alteration Sample.

Figure 22: A ManiVidLens Case on a Material Modification Sample.

Figure 23: A ManiVidLens Case on a Material Modification Sample.

Figure 24: A ManiVidLens Case on a Portrait Editing Sample.

Figure 25: A ManiVidLens Case on a Portrait Editing Sample.

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Yue Feng, Jingjing Li, Qijia Lu +9

MoE Key Laboratory of Brain-Machine Intelligence Technology, College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics · Dalian University of Technology · Independent Scholar +2

Digital Forensics Deepfake Detection Long-Video Understanding Temporal Video Forgery Localization

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Abstract

Video Forensics

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