百万级真实度视频中检测微小伪造片段，推动反深度伪造技术发展。
1M-Deepfakes Detection Challenge
- 基于百万级真实视频数据集，挑战复杂伪造检测与定位。
- 覆盖2000+人物、100万+伪造视频，验证模型泛化能力。
- 适合研究深度伪造检测、多模态安全的学者与工程师。
深度伪造内容的检测与定位，特别是在小段伪造内容与真实视频无缝融合的情况下，仍是数字媒体安全领域的重要挑战。基于最近发布的AV-Deepfake1M数据集（包含超过100万条篡改视频，涉及超过2000名人物），我们推出了1M-Deepfakes检测挑战赛。该挑战旨在激发科研界开发先进方法，以在大规模、高真实感音视频数据集中检测并定位深度伪造内容。参与者可访问AV-Deepfake1M数据集，并提交推理结果以评估检测或定位任务的表现。通过该挑战所开发的方法将推动下一代深度伪造检测与定位系统的发展。评估脚本、基线模型及配套代码将发布于https://github.com/ControlNet/AV-Deepfake1M。
原文摘要 · Abstract (English)
The detection and localization of deepfake content, particularly when small fake segments are seamlessly mixed with real videos, remains a significant challenge in the field of digital media security. Based on the recently released AV-Deepfake1M dataset, which contains more than 1 million manipulated videos across more than 2,000 subjects, we introduce the 1M-Deepfakes Detection Challenge. This challenge is designed to engage the research community in developing advanced methods for detecting and localizing deepfake manipulations within the large-scale high-realistic audio-visual dataset. The participants can access the AV-Deepfake1M dataset and are required to submit their inference results for evaluation across the metrics for detection or localization tasks. The methodologies developed through the challenge will contribute to the development of next-generation deepfake detection and localization systems. Evaluation scripts, baseline models, and accompanying code will be available on https://github.com/ControlNet/AV-Deepfake1M.
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