The detection and localization of highly realistic deepfake audio-visual
content are challenging even for the most advanced state-of-the-art methods.
While most of the research efforts in this domain are focused on detecting
high-quality deepfake images and videos, only a few works address the problem
of the localization of small segments of audio-visual manipulations embedded in
real videos. In this research, we emulate the process of such content
generation and propose the AV-Deepfake1M dataset. The dataset contains
content-driven (i) video manipulations, (ii) audio manipulations, and (iii)
audio-visual manipulations for more than 2K subjects resulting in a total of
more than 1M videos. The paper provides a thorough description of the proposed
data generation pipeline accompanied by a rigorous analysis of the quality of
the generated data. The comprehensive benchmark of the proposed dataset
utilizing state-of-the-art deepfake detection and localization methods
indicates a significant drop in performance compared to previous datasets. The
proposed dataset will play a vital role in building the next-generation
deepfake localization methods. The dataset and associated code are available at
this https URL .

本研究针对高度逼真的深度伪造音视频内容的检测和定位问题提出了 AV-Deepfake1M 数据集，并通过全面的描述和严格的数据质量分析，使用先进的深度伪造检测和定位方法进行了综合基准测试，结果显示与之前数据集相比性能大幅下降。该数据集将在构建下一代深度伪造定位方法中发挥重要作用。