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ARXIV:2603.18625 · AI-GENERATED CONTENT DETECTION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.18625AI-GENERATED CONTENT DETECTIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEYueying Zou · Pei Pei Li · Zekun Li · Xinyu Guo · Xing Cui · Huaibo Huang · +1 at arXiv
A fine-grained benchmark and evaluation framework to diagnose and improve AI-generated video detection capabilities of Large Vision-Language Models.
Opportunity summary
Pain A fine-grained benchmark and evaluation framework to diagnose and improve AI-generated video detection capabilities of Large Vision-Language Models.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A fine-grained benchmark and evaluation framework to diagnose and improve AI-generated video detection capabilities of Large Vision-Language Models. Meanwhile, Large Vision-Language Models (LVLMs) have shown strong potential for detecting such content.
In recent years, AI-generated videos have become increasingly realistic and sophisticated. Meanwhile, Large Vision-Language Models (LVLMs) have shown strong potential for detecting such content.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To address this limitation, we introduce GenVideoLens, a fine-grained benchmark that enables dimension-wise evaluation of LVLM capabilities in AI-generated video detection. Code availability is…
AI-Generated Content Detection moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A fine-grained benchmark and evaluation framework to diagnose and improve AI-generated video detection capabilities of Large Vision-Language Models.
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10.48550/arXiv.2603.18625A fine-grained benchmark and evaluation framework to diagnose and improve AI-generated video detection capabilities of Large Vision-Language Models.
Abstract
In recent years, AI-generated videos have become increasingly realistic and sophisticated. Meanwhile, Large Vision-Language Models (LVLMs) have shown strong potential for detecting such content. However, existing evaluation protocols largely treat the task as a binary classification problem and rely on coarse-grained metrics such as overall accuracy, providing limited insight into where LVLMs succeed or fail. To address this limitation, we introduce GenVideoLens, a fine-grained benchmark that enables dimension-wise evaluation of LVLM capabilities in AI-generated video detection. The benchmark contains 400 highly deceptive AI-generated videos and 100 real videos, annotated by experts across 15 authenticity dimensions covering perceptual, optical, physical, and temporal cues. We evaluate eleven representative LVLMs on this benchmark. Our analysis reveals a pronounced dimensional imbalance. While LVLMs perform relatively well on perceptual cues, they struggle with optical consistency, physical interactions, and temporal-causal reasoning. Model performance also varies substantially across dimensions, with smaller open-source models sometimes outperforming stronger proprietary models on specific authenticity cues. Temporal perturbation experiments further show that current LVLMs make limited use of temporal information. Overall, GenVideoLens provides diagnostic insights into LVLM behavior, revealing key capability gaps and offering guidance for improving future AI-generated video detection systems.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
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Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 7.0
PROBLEM
A fine-grained benchmark and evaluation framework to diagnose and improve AI-generated video detection capabilities of Large Vision-Language Models. Meanwhile, Large Vision-Language Models (LVLMs) have shown strong potential for detecting such content.
METHOD
In recent years, AI-generated videos have become increasingly realistic and sophisticated. Meanwhile, Large Vision-Language Models (LVLMs) have shown strong potential for detecting such content.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To address this limitation, we introduce GenVideoLens, a fine-grained benchmark that enables dimension-wise evaluation of LVLM capabilities in AI-generated video detection. Code availability is flagged in...
WHY NOW
AI-Generated Content Detection moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A fine-grained benchmark and evaluation framework to diagnose and improve AI-generated video detection capabilities of Large Vision-Language Models. Meanwhile, Large Vision-Language Models (LVLMs) have shown strong potential for detecting such content.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
In recent years, AI-generated videos have become increasingly realistic and sophisticated. Meanwhile, Large Vision-Language Models (LVLMs) have shown strong potential for detecting such content.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To address this limitation, we introduce GenVideoLens, a fine-grained benchmark that enables dimension-wise evaluation of LVLM capabilities in AI-generated video detection. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI-Generated Content Detection moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A fine-grained benchmark and evaluation framework to diagnose and improve AI-generated video detection capabilities of Large Vision-Language Models.
Segment
AI-Generated Content Detection
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Commercial read
7.0/10 public viability
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reason
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unverified
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