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Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics

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arxiv 1909.12962 v4 pith:BTUUGT5H submitted 2019-09-27 cs.CR cs.CVeess.IV

classification cs.CRcs.CVeess.IV
keywords deepfakeceleb-dfdatasetslarge-scalevideoschallengingdatasetdetection
verification ladder T0 review T1 audit T2 compute T3 formal
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AI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging problem threatening the trustworthiness of online information. The need to develop and evaluate DeepFake detection algorithms calls for large-scale datasets. However, current DeepFake datasets suffer from low visual quality and do not resemble DeepFake videos circulated on the Internet. We present a new large-scale challenging DeepFake video dataset, Celeb-DF, which contains 5,639 high-quality DeepFake videos of celebrities generated using improved synthesis process. We conduct a comprehensive evaluation of DeepFake detection methods and datasets to demonstrate the escalated level of challenges posed by Celeb-DF.

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 104 citations worldwide. Full citation record

  1. Detecting Deepfake Talking Heads from Facial Biometric Anomalies

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A lightweight XGBoost classifier trained on statistical moments of pairwise ArcFace biometric similarities can distinguish real talking-head videos from face-swap and lip-sync deepfakes with around 95% accuracy on mat...

  2. What is Adversarial Training for Diffusion Models?

    cs.CV 2025-05 conditional novelty 5.0 of 10

    Diffusion models trained with an equivariant adversarial-smoothing regularizer tolerate heavy training-data corruption but lose image quality on clean data.

  3. Visual Language Models as Zero-Shot Deepfake Detectors

    cs.CV 2025-07 conditional novelty 4.0 of 10

    Zero-shot VLMs scored by normalized yes/no token probabilities beat most trained deepfake detectors on a new SimSwap dataset, and a lightly fine-tuned InstructBLIP is near-perfect on DFDC-P.

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