The DeepFake Detection Challenge (DFDC) Dataset.
By Brian Dolhansky, Joanna Bitton, Ben Pflaum, Jikuo Lu, Russ Howes, Menglin Wang, Cristian Canton Ferrer. 2020
951 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/77810e84d07b55818f00b9c22defbf82eccb43bb
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Citations in the corpus
- A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials (2022, background): “…✗ UADFW[80] EBVdataset[44] KnownDeepfaketech ✗ FaceForensics++[64] YouTube KnownDeepfaketech ✗ Celab-DFv2[47] YouTube KnownDeepfaketech ✗ DFDC[19] Actors KnownDeepfaketech ✗ WildDeepfake[89] Internet UnknownDeepfaketech ✗ WhichFaceReal[3] Internet UnknownDeepfaketech ✗ CNNfake[76]…”
- M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection (2021, methodology): “(a) FF++ [48] (b) DFD [9] (c) DFDC [13] (d) Celeb-DF [35]”
- End-to-End Reconstruction-Classification Learning for Face Forgery Detection (2022, methodology): “For the FaceForensics++ [11], Celeb-DF [7] and DFDC [3] datasets, we use RetinaFace [2] to extract the face region from video sequences and adopt a conservative crop which enlarges the facial region by a factor of 1.”
- Video Transformer for Deepfake Detection with Incremental Learning (2021, methodology): “Finally, we � ne-tune the trained model on 6,000 DFDC images, which are also 0.05% of the DFDC dataset [21].”