title: DeMamba: AI-Generated Video Detection on Million-Scale GenVideo
    Benchmark
summary: 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.
paper_authors._id: 6659eb0e6d6405b550374560
paper_authors.name: Haoxing Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-31T19:52:16.034Z
paper_authors._id: 63898c562a897944ea5f07a2
paper_authors.avatarUrl: /avatars/75b42fbc20771dea5d61875bf4541042.svg
paper_authors.fullname: Haoxing chen
paper_authors.type: user
paper_authors.user: HaoxingChen
paper_authors._id: 6659eb0e6d6405b550374561
paper_authors.name: Yan Hong
paper_authors._id: 6659eb0e6d6405b550374562
paper_authors.name: Zizheng Huang
paper_authors._id: 6659eb0e6d6405b550374563
paper_authors.name: Zhuoer Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-31T19:52:37.330Z
paper_authors._id: 63fdf1556315a264aba75587
paper_authors.avatarUrl: /avatars/acec74e9196e09cc722d371e9fb221c3.svg
paper_authors.fullname: ZhuoerXu
paper_authors.type: user
paper_authors.user: Zhuoer
paper_authors._id: 6659eb0e6d6405b550374564
paper_authors.name: Zhangxuan Gu
paper_authors._id: 6659eb0e6d6405b550374565
paper_authors.name: Yaohui Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-31T19:52:52.046Z
paper_authors._id: 6506f955383b7e031fa62b0b
paper_authors.avatarUrl: /avatars/2ae08173f4e8687a33c682caa6b832a1.svg
paper_authors.fullname: Yaohui Li
paper_authors.type: user
paper_authors.user: TantieNiu
paper_authors._id: 6659eb0e6d6405b550374566
paper_authors.name: Jun Lan
paper_authors._id: 6659eb0e6d6405b550374567
paper_authors.name: Huijia Zhu
paper_authors._id: 6659eb0e6d6405b550374568
paper_authors.name: Jianfu Zhang
paper_authors._id: 6659eb0e6d6405b550374569
paper_authors.name: Weiqiang Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-31T19:53:43.329Z
paper_authors._id: 65817e9ee77395a0c87c9d30
paper_authors.avatarUrl: /avatars/f19f3c10e97df7dac8d3a58f41275c4c.svg
paper_authors.fullname: weiqiang wang
paper_authors.type: user
paper_authors.user: weyl
paper_authors._id: 6659eb0e6d6405b55037456a
paper_authors.name: Huaxiong Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-31T19:53:50.589Z
paper_authors._id: 64cf5a387a7305c5891e48d9
paper_authors.avatarUrl: /avatars/d074b14b5228900872631f7948cf60e7.svg
paper_authors.fullname: Huaxiong Li
paper_authors.type: user
paper_authors.user: huaxiongli
paper_title: DeMamba: AI-Generated Video Detection on Million-Scale GenVideo
    Benchmark
paper_summary: 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.
paper_ai_keywords: AI-generated video detection dataset GenVideo video classification task degraded video classification task temporal and spatial dimensions Detail Mamba (DeMamba) generalizability robustness