# KoDF: A Large-scale Korean DeepFake Detection Dataset

arXiv: https://arxiv.org/abs/2103.10094 · HTML: https://mlsanity.com/paper/2103.10094/

## Key facts

- Cited by 80 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 55 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Builds on (most cited first, 29 of 29)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [First Order Motion Model for Image Animation](https://mlsanity.com/paper/2003.00196/) (2020)
- [A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild](https://mlsanity.com/paper/2008.10010/) (2020)
- [Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics](https://mlsanity.com/paper/1909.12962/) (2019)
- [How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)](https://mlsanity.com/paper/1703.07332/) (2017)
- [FSGAN: Subject Agnostic Face Swapping and Reenactment](https://mlsanity.com/paper/1908.05932/) (2019)
- [Exposing Deep Fakes Using Inconsistent Head Poses](https://mlsanity.com/paper/1811.00661/) (2018)
- [DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection](https://mlsanity.com/paper/2001.03024/) (2020)
- [DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection](https://mlsanity.com/paper/2001.00179/) (2020)
- [Lip Reading Sentences in the Wild](https://mlsanity.com/paper/1611.05358/) (2016)
- [DeepFakes: a New Threat to Face Recognition? Assessment and Detection](https://mlsanity.com/paper/1812.08685/) (2018)
- [The Deepfake Detection Challenge (DFDC) Preview Dataset](https://mlsanity.com/paper/1910.08854/) (2019)
- [Two-branch Recurrent Network for Isolating Deepfakes in Videos](https://mlsanity.com/paper/2008.03412/) (2020)
- [Recurrent Convolutional Strategies for Face Manipulation Detection in Videos](https://mlsanity.com/paper/1905.00582/) (2019)
- [Audio-driven Talking Face Video Generation with Learning-based Personalized Head Pose](https://mlsanity.com/paper/2002.10137/) (2020)
- [Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples](https://mlsanity.com/paper/2002.12749/) (2020)
- [FDA: Feature Disruptive Attack](https://mlsanity.com/paper/1909.04385/) (2019)
- [Sharp Multiple Instance Learning for DeepFake Video Detection](https://mlsanity.com/paper/2008.04585/) (2020)
- [Adversarial Perturbations Fool Deepfake Detectors](https://mlsanity.com/paper/2003.10596/) (2020)
- [Adversarial Threats to DeepFake Detection: A Practical Perspective](https://mlsanity.com/paper/2011.09957/) (2020)
- [DeepFakesON-Phys: DeepFakes Detection based on Heart Rate Estimation](https://mlsanity.com/paper/2010.00400/) (2020)
- [DeepFakes Evolution: Analysis of Facial Regions and Fake Detection Performance](https://mlsanity.com/paper/2004.07532/) (2020)
- [Advbox: a toolbox to generate adversarial examples that fool neural networks](https://mlsanity.com/paper/2001.05574/) (2020)
- [VideoForensicsHQ: Detecting High-quality Manipulated Face Videos](https://mlsanity.com/paper/2005.10360/) (2020)

## Built on by (most cited first, 30 of 80)

- [FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset](https://mlsanity.com/paper/2108.05080/) (2021)
- [DF40: Toward Next-Generation Deepfake Detection](https://mlsanity.com/paper/2406.13495/) (2024)
- [TALL: Thumbnail Layout for Deepfake Video Detection](https://mlsanity.com/paper/2307.07494/) (2023)
- [Self-Supervised Video Forensics by Audio-Visual Anomaly Detection](https://mlsanity.com/paper/2301.01767/) (2023)
- [AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset](https://mlsanity.com/paper/2311.15308/) (2023)
- [Deepfake Generation and Detection: A Benchmark and Survey](https://mlsanity.com/paper/2403.17881/) (2024)
- [BlendFace: Re-designing Identity Encoders for Face-Swapping](https://mlsanity.com/paper/2307.10854/) (2023)
- [BiHPF: Bilateral High-Pass Filters for Robust Deepfake Detection](https://mlsanity.com/paper/2109.00911/) (2021)
- [AVFF: Audio-Visual Feature Fusion for Video Deepfake Detection](https://mlsanity.com/paper/2406.02951/) (2024)
- [Multimodal Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2305.13507/) (2023)
- [Deepfake Detection: A Comprehensive Survey from the Reliability Perspective](https://mlsanity.com/paper/2211.10881/) (2022)
- [Community Forensics: Using Thousands of Generators to Train Fake Image Detectors](https://mlsanity.com/paper/2411.04125/) (2024)
- [Audio-Visual Person-of-Interest DeepFake Detection](https://mlsanity.com/paper/2204.03083/) (2022)
- [Glitch in the Matrix: A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localization](https://mlsanity.com/paper/2305.01979/) (2023)
- [BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanation](https://mlsanity.com/paper/2505.12620/) (2025)
- [Evaluation of an Audio-Video Multimodal Deepfake Dataset using Unimodal and Multimodal Detectors](https://mlsanity.com/paper/2109.02993/) (2021)
- [BusterX++: Towards Unified Cross-Modal AI-Generated Content Detection and Explanation with MLLM](https://mlsanity.com/paper/2507.14632/) (2025)
- [Circumventing shortcuts in audio-visual deepfake detection datasets with unsupervised learning](https://mlsanity.com/paper/2412.00175/) (2024)
- [Learning Spatiotemporal Inconsistency via Thumbnail Layout for Face Deepfake Detection](https://mlsanity.com/paper/2403.10261/) (2024)
- [Celeb-DF++: A Large-scale Challenging Video DeepFake Benchmark for Generalizable Forensics](https://mlsanity.com/paper/2507.18015/) (2025)
- [Deepfake Detection that Generalizes Across Benchmarks](https://mlsanity.com/paper/2508.06248/) (2025)
- [Deep Convolutional Pooling Transformer for Deepfake Detection](https://mlsanity.com/paper/2209.05299/) (2022)
- [Digital and Physical Face Attacks: Reviewing and One Step Further](https://mlsanity.com/paper/2209.14692/) (2022)
- [Detecting Deepfakes Without Seeing Any](https://mlsanity.com/paper/2311.01458/) (2023)
- [PolyGlotFake: A Novel Multilingual and Multimodal DeepFake Dataset](https://mlsanity.com/paper/2405.08838/) (2024)
- [The Age of Synthetic Realities: Challenges and Opportunities](https://mlsanity.com/paper/2306.11503/) (2023)
- [Exposing Lip-syncing Deepfakes from Mouth Inconsistencies](https://mlsanity.com/paper/2401.10113/) (2024)
- [Analyzing Fairness in Deepfake Detection With Massively Annotated Databases](https://mlsanity.com/paper/2208.05845/) (2022)
- [A Survey of Defenses Against AI-Generated Visual Media: Detection,Disruption, and Authentication](https://mlsanity.com/paper/2407.10575/) (2024)
- [Beyond Spatial Frequency: Pixel-wise Temporal Frequency-based Deepfake Video Detection](https://mlsanity.com/paper/2507.02398/) (2025)

## Abstract (arXiv)

A variety of effective face-swap and face-reenactment methods have been publicized in recent years, democratizing the face synthesis technology to a great extent. Videos generated as such have come to be called deepfakes with a negative connotation, for various social problems they have caused. Facing the emerging threat of deepfakes, we have built the Korean DeepFake Detection Dataset (KoDF), a large-scale collection of synthesized and real videos focused on Korean subjects. In this paper, we provide a detailed description of methods used to construct the dataset, experimentally show the discrepancy between the distributions of KoDF and existing deepfake detection datasets, and underline the importance of using multiple datasets for real-world generalization. KoDF is publicly available at https://moneybrain-research.github.io/kodf in its entirety (i.e. real clips, synthesized clips, clips with adversarial attack, and metadata).