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. Featuring eight facial modification algorithms

Dataset

This dataset was created by Johnny Lee

Released under CC0: Public Domain

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Dataset

This dataset was created by Fakecatcher AI

Released under CC0: Public Domain

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laodeAlif/dfdc dataset hosted on Hugging Face and contributed by the HF Datasets community

The DFDC dataset contains 100,000 images of faces manipulated using Deepfakes.

Dataset

This dataset was created by Madhav Deshatwad

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Dataset

This dataset was created by itsmellslikeml

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Explore the historical Whois records related to dfdc.info (Domain). Get insights into ownership history and changes over time.

The Deepfake face detection task involves a facial image of unknown authenticity for testing. While most deepfake detection methods take only the image as input, our literature demonstrates that conditioning the deepfake detector on identity—i.e., knowing whose deepfake face the picture might be—can enhance detection performance. Existing deepfake detection datasets, such as FaceForensics++ and DFDC, do not include identity information for authentic and deepfake faces. This dataset contains facial images of 45 specific individuals, divided into train and test sets, including a total of 23k authentic and 22k deepfake images. Having a specific individual's images in both the train and test sets allows us to assess detection performance for that individual. The dataset is curated so that the train and test sets are from two independent sources. The train images are curated from the CelebDFv2 dataset, and the test images are curated from the CACD dataset. Deepfake faces are generated using FaceswapGAN, utilizing a portion of the training images to train the reconstruction model. The test deepfake images are faceswapped with another identity not included in our celebrity list. On the other hand, the training deepfake images are reconstructed images of that person. The deepfake detection method proposed in our paper requires reconstructing both the training and test images. The reconstructed test and train images are also available in this dataset. It is worth mentioning that reconstructing the training deepfake images produces doubly reconstructed images.

Comparison between different combinations of Mixformer. The results in the table are test with the DFDC dataset (in %).

The global Drive Force Distribution Controller (DFDC) market is experiencing robust growth, driven by the increasing demand for advanced driver-assistance systems (ADAS) and the rising adoption of electric and hybrid vehicles. The market, estimated at $8 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated value of $25 billion by 2033. This significant expansion is fueled by several key factors. The integration of DFDCs in vehicles enhances safety, stability, and fuel efficiency, making them increasingly crucial for both passenger cars and commercial vehicles. Technological advancements, such as the development of more sophisticated electronic control units (ECUs) and improved algorithms, are further driving market growth. The trend toward autonomous driving is also contributing significantly, as DFDCs are essential components in enabling precise vehicle control in autonomous driving scenarios. Segmentation reveals that the electronic DFDC segment holds a larger market share compared to mechanical counterparts due to superior performance and enhanced control capabilities. The passenger car segment currently dominates application-based segmentation but commercial vehicles are catching up owing to stricter safety regulations and improved fuel economy demands. Key players such as Bosch, Continental, and ZF Friedrichshafen are investing heavily in R&D to stay ahead of the competition, leading to continuous innovation within the industry. Despite the positive outlook, the market faces certain restraints. High initial costs associated with DFDC integration can be a barrier for some manufacturers, particularly in developing regions. Furthermore, the complex integration process and the need for specialized expertise pose challenges. However, the long-term benefits in terms of improved safety, fuel efficiency, and enhanced driving experience are expected to outweigh these challenges, ensuring continued market growth. The geographical distribution reveals strong growth potential in Asia Pacific, driven by increasing vehicle production and rising disposable incomes in major economies like China and India. North America and Europe are also significant markets, with established automotive industries and a high demand for advanced vehicle technologies.

Dataset

This dataset was created by Harsh Chinchakar

Released under Apache 2.0

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Dataset

This dataset was created by Caleb

Released under CC0: Public Domain

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The global Drive Force Distribution Controller (DFDC) market is experiencing robust growth, driven by the increasing demand for advanced driver-assistance systems (ADAS) and the rising adoption of electric and hybrid vehicles. The market, valued at approximately $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated market size of $7.2 billion by 2033. This growth is fueled by several key factors, including stringent government regulations mandating improved vehicle safety and fuel efficiency, the integration of DFDCs into increasingly sophisticated vehicle control units, and the burgeoning popularity of autonomous driving technologies. The electronic segment currently dominates the market due to the advantages of enhanced precision and control compared to mechanical systems, leading to improved vehicle handling and stability. The passenger car segment holds a larger market share compared to commercial vehicles, although commercial vehicle adoption is projected to increase substantially in the coming years, driven by fleet optimization demands and safety improvements. Key players like Bosch, Continental, and ZF Friedrichshafen are actively investing in R&D to develop advanced DFDC technologies that meet the evolving needs of the automotive industry. The geographic distribution of the market reflects the global trends in vehicle manufacturing and technological adoption. North America and Europe currently hold significant market shares, driven by high vehicle ownership rates and established automotive industries. However, rapid growth is anticipated in the Asia-Pacific region, particularly in China and India, driven by increasing automotive production and growing demand for advanced safety features in newly manufactured vehicles. While challenges such as high initial investment costs and the complexities of integration with other vehicle systems exist, the long-term benefits in terms of enhanced safety, fuel efficiency, and driving experience are projected to outweigh these restraints, ensuring sustained market expansion throughout the forecast period. Competition among existing players is intense, with companies focusing on technological innovation, strategic partnerships, and geographic expansion to maintain their market positions.

such as FaceForensics++ and DFDC

Dataset

This dataset was created by Wahab Arabo

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Building plan “Bulzen I — 10” transformed according to INSPIRE. + 11. Change’ of the city of Spaichingen based on an XPlanung dataset in version 5.0.

Dataset

This dataset was created by Rimjhim Sinha

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FaceForensics++ is a forensics dataset consisting of 1000 original video sequences that have been manipulated with four automated face manipulation methods: Deepfakes, Face2Face, FaceSwap and NeuralTextures. The data has been sourced from 977 youtube videos and all videos contain a trackable mostly frontal face without occlusions which enables automated tampering methods to generate realistic forgeries.

Dataset

This dataset was created by Hieu Phung

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