DeMamba: AI-Generated Video Detection on Million-Scale GenVideo
  Benchmark
Recently, video generation techniques have advanced rapidly. Given the
popularity of video content on social media platforms, these models intensify
concerns about the spread of fake information. Therefore, there is a growing
demand for detectors capable of distinguishing between fake AI-generated videos
and mitigating the potential harm caused by fake information. However, the lack
of large-scale datasets from the most advanced video generators poses a barrier
to the development of such detectors. To address this gap, we introduce the
first AI-generated video detection dataset, GenVideo. It features the following
characteristics: (1) a large volume of videos, including over one million
AI-generated and real videos collected; (2) a rich diversity of generated
content and methodologies, covering a broad spectrum of video categories and
generation techniques. We conducted extensive studies of the dataset and
proposed two evaluation methods tailored for real-world-like scenarios to
assess the detectors' performance: the cross-generator video classification
task assesses the generalizability of trained detectors on generators; the
degraded video classification task evaluates the robustness of detectors to
handle videos that have degraded in quality during dissemination. Moreover, we
introduced a plug-and-play module, named Detail Mamba (DeMamba), designed to
enhance the detectors by identifying AI-generated videos through the analysis
of inconsistencies in temporal and spatial dimensions. Our extensive
experiments demonstrate DeMamba's superior generalizability and robustness on
GenVideo compared to existing detectors. We believe that the GenVideo dataset
and the DeMamba module will significantly advance the field of AI-generated
video detection. Our code and dataset will be aviliable at
https://github.com/chenhaoxing/DeMamba.
6659eb0e6d6405b550374560
Haoxing Chen
admin_assigned
2024-05-31T19:52:16.034Z
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Haoxing chen
user
HaoxingChen
6659eb0e6d6405b550374561
Yan Hong
6659eb0e6d6405b550374562
Zizheng Huang
6659eb0e6d6405b550374563
Zhuoer Xu
admin_assigned
2024-05-31T19:52:37.330Z
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ZhuoerXu
user
Zhuoer
6659eb0e6d6405b550374564
Zhangxuan Gu
claimed_verified
2025-07-23T08:39:26.022Z
60d2a2984956988b63753371
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zhangxgu
user
zhangxgu
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Yaohui Li
admin_assigned
2024-05-31T19:52:52.046Z
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Yaohui Li
user
TantieNiu
6659eb0e6d6405b550374566
Jun Lan
6659eb0e6d6405b550374567
Huijia Zhu
6659eb0e6d6405b550374568
Jianfu Zhang
6659eb0e6d6405b550374569
Weiqiang Wang
admin_assigned
2024-05-31T19:53:43.329Z
65817e9ee77395a0c87c9d30
/avatars/f19f3c10e97df7dac8d3a58f41275c4c.svg
weyl
user
weyl
6659eb0e6d6405b55037456a
Huaxiong Li
admin_assigned
2024-05-31T19:53:50.589Z
64cf5a387a7305c5891e48d9
/avatars/d074b14b5228900872631f7948cf60e7.svg
Huaxiong Li
user
huaxiongli
DeMamba: AI-Generated Video Detection on Million-Scale GenVideo
  Benchmark
Recently, video generation techniques have advanced rapidly. Given the
popularity of video content on social media platforms, these models intensify
concerns about the spread of fake information. Therefore, there is a growing
demand for detectors capable of distinguishing between fake AI-generated videos
and mitigating the potential harm caused by fake information. However, the lack
of large-scale datasets from the most advanced video generators poses a barrier
to the development of such detectors. To address this gap, we introduce the
first AI-generated video detection dataset, GenVideo. It features the following
characteristics: (1) a large volume of videos, including over one million
AI-generated and real videos collected; (2) a rich diversity of generated
content and methodologies, covering a broad spectrum of video categories and
generation techniques. We conducted extensive studies of the dataset and
proposed two evaluation methods tailored for real-world-like scenarios to
assess the detectors' performance: the cross-generator video classification
task assesses the generalizability of trained detectors on generators; the
degraded video classification task evaluates the robustness of detectors to
handle videos that have degraded in quality during dissemination. Moreover, we
introduced a plug-and-play module, named Detail Mamba (DeMamba), designed to
enhance the detectors by identifying AI-generated videos through the analysis
of inconsistencies in temporal and spatial dimensions. Our extensive
experiments demonstrate DeMamba's superior generalizability and robustness on
GenVideo compared to existing detectors. We believe that the GenVideo dataset
and the DeMamba module will significantly advance the field of AI-generated
video detection. Our code and dataset will be aviliable at
https://github.com/chenhaoxing/DeMamba.
A new dataset, GenVideo, and a module named Detail Mamba enhance the detection of AI-generated videos, addressing the lack of large-scale datasets and improving detector performance across diverse video generations.
AI-generated video detection GenVideo video dataset cross-generator video classification degraded video classification Detail Mamba DeMamba temporal dimensions spatial dimensions AI-generated videos video generators video dissemination