We partnered with other industry leaders and academic experts in September 2019 to create the Deepfake Detection Challenge (DFDC) in order to accelerate development of new ways to detect deepfake videos. In doing so, we created and shared a unique new dataset for the challenge consisting of more than 100,000 videos.

The DFDC (Deepfake Detection Challenge) is a dataset for deepface detection consisting of more than 100,000 videos. The DFDC dataset consists of two versions: - Preview dataset. with 5k videos. Featuring two facial modification algorithms. - Full dataset, with 124k videos.

2020年6月12日 · By creating and sharing a unique new dataset of more than 100,000 videos, the DFDC has enabled experts from around the world to come together, benchmark their deepfake detection models, try new approaches, and learn from each others’ work.

2020年6月12日 · To counter this emerging threat, we have constructed an extremely large face swap video dataset to enable the training of detection models, and organized the accompanying DeepFake Detection Challenge (DFDC) Kaggle competition.

We trained 7 different 3D CNNs across 4 different architectures (I3D, 3D ResNet34, MC3 & R2+1D) and 2 different resolutions (224 x 224 & 112 x 112). The validation losses of the 3D CNNs ranged between 0.1374 and 0.1905.

2019年10月19日 · In this paper, we introduce a preview of the Deepfakes Detection Challenge (DFDC) dataset consisting of 5K videos featuring two facial modification algorithms. A data collection campaign has been carried out where participating actors have entered into an agreement to the use and manipulation of their likenesses in our creation of the dataset.

2019年12月11日 · We’re taking an important step forward today with the launch of the Deepfake Detection Challenge (DFDC), an open, collaborative initiative to accelerate development of new technologies for detecting deepfakes and manipulated media.

2024年3月3日 · To counter this emerging threat, we have constructed an extremely large face swap video dataset to enable the training of detection models, and organized the accompanying DeepFake Detection Challenge (DFDC) Kaggle competition.

Solution for the Deepfake Detection Challenge. Our solution consists of three EfficientNet-B7 models (we used the Noisy Student pre-trained weights). We did not use external data, except for pre-trained weights. One model runs on frame sequences (a 3D convolution has been added to each EfficientNet-B7 block).

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