The generative model has made significant advancements in the creation of
realistic videos, which causes security issues. However, this emerging risk has
not been adequately addressed due to the absence of a benchmark dataset for
AI-generated videos. In this paper, we first construct a video dataset using
advanced diffusion-based video generation algorithms with various semantic
contents. Besides, typical video lossy operations over network transmission are
adopted to generate degraded samples. Then, by analyzing local and global
temporal defects of current AI-generated videos, a novel detection framework by
adaptively learning local motion information and global appearance variation is
constructed to expose fake videos. Finally, experiments are conducted to
evaluate the generalization and robustness of different spatial and temporal
domain detection methods, where the results can serve as the baseline and
demonstrate the research challenge for future studies.

本文构建了一个视频数据集，使用先进的扩散式视频生成算法和多种语义内容，并采用 typcial video lossy 操作生成退化样本。随后，通过分析当前 AI 生成视频的局部和全局时间缺陷，构建了一个新的检测框架，通过自适应学习局部运动信息和全局外观变化来暴露伪造视频。最后，进行实验评估了不同空间和时间域检测方法的泛化性和鲁棒性，结果可以作为基线，并展示未来研究的挑战。

揭示人工智能生成的视频：基于本地和全局时间缺陷的检测方法与基准数据集

Exposing AI-generated Videos: A Benchmark Dataset and a Local-and-Global  Temporal Defect Based Detection Method

This paper presents a new approach for the detection of fake videos, based on
the analysis of style latent vectors and their abnormal behavior in temporal
changes in the generated videos. We discovered that the generated facial videos
suffer from the temporal distinctiveness in the temporal changes of style
latent vectors, which are inevitable during the generation of temporally stable
videos with various facial expressions and geometric transformations. Our
framework utilizes the StyleGRU module, trained by contrastive learning, to
represent the dynamic properties of style latent vectors. Additionally, we
introduce a style attention module that integrates StyleGRU-generated features
with content-based features, enabling the detection of visual and temporal
artifacts. We demonstrate our approach across various benchmark scenarios in
deepfake detection, showing its superiority in cross-dataset and
cross-manipulation scenarios. Through further analysis, we also validate the
importance of using temporal changes of style latent vectors to improve the
generality of deepfake video detection.

基于分析样式潜向量的异常行为在生成视频的时间变化中，本文提出了一种检测伪造视频的新方法。通过使用 StyleGRU 模块和样式注意模块，结合动态属性的样式潜向量，实现了对视觉和时间伪迹的检测，并在深度伪造检测中展示了其优越性，尤其在跨数据集和跨篡改场景中。此外，进一步分析验证了利用时间变化的样式潜向量来提高深度伪造视频检测的普适性的重要性。

利用样式潜流进行深度假像检测视频检测的泛化

Exploiting Style Latent Flows for Generalizing Deepfake Detection Video  Detection

Fake videos represent an important misinformation threat. While existing
forensic networks have demonstrated strong performance on image forgeries,
recent results reported on the Adobe VideoSham dataset show that these networks
fail to identify fake content in videos. In this paper, we show that this is
due to video coding, which introduces local variation into forensic traces. In
response, we propose VideoFACT - a new network that is able to detect and
localize a wide variety of video forgeries and manipulations. To overcome
challenges that existing networks face when analyzing videos, our network
utilizes both forensic embeddings to capture traces left by manipulation,
context embeddings to control for variation in forensic traces introduced by
video coding, and a deep self-attention mechanism to estimate the quality and
relative importance of local forensic embeddings. We create several new video
forgery datasets and use these, along with publicly available data, to
experimentally evaluate our network's performance. These results show that our
proposed network is able to identify a diverse set of video forgeries,
including those not encountered during training. Furthermore, we show that our
network can be fine-tuned to achieve even stronger performance on challenging
AI-based manipulations.

该研究提出了一种新的网络 VideoFACT，能够检测和定位多种视频伪造和操纵，包括那些未在训练中遇到的，并能在具有挑战性的基于人工智能的操纵上取得更强的性能表现。