Dataset

This dataset was created by Fakecatcher AI

Released under CC0: Public Domain

Contents

The DeepFake Detection Challenge (DFDC) dataset contains over 100,000 videos, including authentic and manipulated content.

Dataset

This dataset was created by itsmellslikeml

Contents

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

Face forgery by deepfake is widely spread over the internet and has raised severe societal concerns. Recently, how to detect such forgery contents has become a hot research topic and many deepfake detection methods have been proposed.

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

Comparison of model performance on the DFDC dataset.

Face embeddings

Using SSD-MobileNet face detector trained on WiderFace dataset, from here and FaceNet trained on VGGFace2, from here, I've detected faces and computed FaceNet embeddings, storing an average embedding per bbox track (for tracking I used a Kalman-based approach SORT, for more details please check this repo.

Dataset

This dataset was created by Johnny Lee

Released under CC0: Public Domain

Contents

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

such as FaceForensics++ and DFDC

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

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

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

Dataset

This dataset was created by Robert Russell

Released under Apache 2.0

Contents

150x150 face images from some frames from every video in part 22 of Deepfake Detection train data set. Specifically 10 frames evenly taken from all parts of every video.

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.

The malicious use of deepfake videos seriously affects information security and brings great harm to society. Currently, deepfake videos are mainly generated based on deep learning methods, which are difficult to be recognized by the naked eye, therefore, it is of great significance to study accurate and efficient deepfake video detection techniques. Most of the existing detection methods focus on analyzing the discriminative information in a specific feature domain for classification from a local or global perspective. Such detection methods based on a single type feature have certain limitations in practical applications. In this paper, we propose a deepfake detection method with the ability to comprehensively analyze the forgery face features, which integrates features in the space domain, noise domain, and frequency domain, and uses the Inception Transformer to learn the mix of global and local information dynamically. We evaluate the proposed method on the DFDC, Celeb-DF, and FaceForensic++ benchmark datasets. Extensive experiments verify the effectiveness and good generalization of the proposed method. Compared with the optimal model, the proposed method with a small number of parameters does not use pre-training, distillation, or assembly, but still achieves competitive performance. The ablation experiments evaluate the role of each component.

Dataset

This dataset was created by Chason

Contents

• The global industry was valued at US$ 6.2 Bn in 2023
• It is estimated to grow at a CAGR of 5.0% from 2024 to 2034 and reach US$ 10.8 Bn by the end of 2034

Market Introduction

Regional Outlook

Fault Detection and Classification (FDC) Market Snapshot