Skyra: AI-Generated Video Detection via Grounded Artifact Reasoning
The misuse of AI-driven video generation technologies has raised serious social concerns, highlighting the urgent need for reliable AI-generated video detectors. However, most existing methods are limited to binary classification and lack the necessary explanations for human interpretation. In this paper, we present Skyra, a specialized multimodal large language model (MLLM) that identifies human-perceivable visual artifacts in AI-generated videos and leverages them as grounded evidence for both detection and explanation. To support this objective, we construct ViF-CoT-4K for Supervised Fine-Tuning (SFT), which represents the first large-scale AI-generated video artifact dataset with fine-grained human annotations. We then develop a two-stage training strategy that systematically enhances our model's spatio-temporal artifact perception, explanation capability, and detection accuracy. To comprehensively evaluate Skyra, we introduce ViF-Bench, a benchmark comprising 3K high-quality samples generated by over ten state-of-the-art video generators. Extensive experiments demonstrate that Skyra surpasses existing methods across multiple benchmarks, while our evaluation yields valuable insights for advancing explainable AI-generated video detection.
69436a60542d62d58a7bf65b
Yifei Li
claimed_verified
2025-12-18T08:00:01.339Z
64adfeac4beffa272dfaef21
/avatars/883f6ba38b993476115dfafcef9ce3c1.svg
Yifei Li
JoeLeelyf
user
JoeLeelyf
69436a60542d62d58a7bf65c
Wenzhao Zheng
69436a60542d62d58a7bf65d
Yanran Zhang
claimed_verified
2025-12-19T09:07:21.101Z
661cfae9a853782abad2a495
/avatars/39723a07bf9efed8278e009fe966d044.svg
Yanran Zhang
Yanran21
user
Yanran21
69436a60542d62d58a7bf65e
Runze Sun
69436a60542d62d58a7bf65f
Yu Zheng
69436a60542d62d58a7bf660
Lei Chen
69436a60542d62d58a7bf661
Jie Zhou
69436a60542d62d58a7bf662
Jiwen Lu
Skyra: AI-Generated Video Detection via Grounded Artifact Reasoning
The misuse of AI-driven video generation technologies has raised serious social concerns, highlighting the urgent need for reliable AI-generated video detectors. However, most existing methods are limited to binary classification and lack the necessary explanations for human interpretation. In this paper, we present Skyra, a specialized multimodal large language model (MLLM) that identifies human-perceivable visual artifacts in AI-generated videos and leverages them as grounded evidence for both detection and explanation. To support this objective, we construct ViF-CoT-4K for Supervised Fine-Tuning (SFT), which represents the first large-scale AI-generated video artifact dataset with fine-grained human annotations. We then develop a two-stage training strategy that systematically enhances our model's spatio-temporal artifact perception, explanation capability, and detection accuracy. To comprehensively evaluate Skyra, we introduce ViF-Bench, a benchmark comprising 3K high-quality samples generated by over ten state-of-the-art video generators. Extensive experiments demonstrate that Skyra surpasses existing methods across multiple benchmarks, while our evaluation yields valuable insights for advancing explainable AI-generated video detection.
Skyra, a specialized multimodal large language model, detects and explains visual artifacts in AI-generated videos using a novel dataset and two-stage training strategy, outperforming existing methods.
multimodal large language model AI-generated video detectors visual artifacts ViF-CoT-4K supervised fine-tuning spatio-temporal artifact perception explanation capability detection accuracy ViF-Bench explainable AI-generated video detection