CCDF: A Benchmark Dataset for Deepfake Detection in Real-World Surveillance Footage

Key points

• Existing deepfake video datasets, used by the research community to develop deepfake detection algorithms, exhibit two limitations: (1) they emphasize benign web content rather than footage of possibly malicious activity, and (2) they rely on older or open-source generators that do not represent recent advances in generative systems.1
• We assemble CCtv DeepFakes (CCDF), a video deepfake dataset, to address both gaps.1
• CCDF is a highly realistic, small-scale, manually annotated dataset targeting evaluation of detection models.1
• We release three versions of the dataset: the raw generated data, a cleaned version in which video metadata are standardized between real and synthetic samples to prevent detectors from exploiting trivial cues, and an altered version simulating low-effort post-processing attacks.1

Sources (1)

• [1]CCDF: A Benchmark Dataset for Deepfake Detection in Real-World Surveillance Footage
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 08:13 AM

  Due to rapid advances in Generative AI, commercial video generation tools can be used to produce fabricated surveillance footage that can fool both human viewers and automated synthetic video detectors.

  Existing deepfake video datasets, used by the research community to develop deepfake detection algorithms, exhibit two limitations: (1) they emphasize benign web content rather than footage of possibly malicious activity, and (2) they rely on older or open-source generators that do not represent recent advances in generative systems.

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